Deep learning method for a progressive lens simulator with an artificial intelligence engine

ABSTRACT

A Progressive Lens Simulator comprises an Eye Tracker, for tracking an eye axis direction to determine a gaze distance, an Off-Axis Progressive Lens Simulator, for generating an Off-Axis progressive lens simulation; and an Axial Power-Distance Simulator, for simulating a progressive lens power in the eye axis direction. The Progressive Lens Simulator can alternatively include an Integrated Progressive Lens Simulator, for creating a Comprehensive Progressive Lens Simulation. The Progressive Lens Simulator can be Head-mounted. A Guided Lens Design Exploration System for the Progressive Lens Simulator can include a Progressive Lens Simulator, a Feedback-Control Interface, and a Progressive Lens Design processor, to generate a modified progressive lens simulation for the patient after a guided modification of the progressive lens design. A Deep Learning Method for an Artificial Intelligence Engine can be used for a Progressive Lens Design Processor. Embodiments include a multi-station system of Progressive Lens Simulators and a Central Supervision Station.

CROSS REFERENCE TO RELATED APPLICATION

The present patent application is a continuation of, and claims benefitfrom, U.S. patent application Ser. No. 15/984,397, entitled:“Progressive lens simulator with an axial power-distance simulator”,filed May 20, 2018, which application is hereby incorporated in itsentirety by reference.

FIELD OF INVENTION

This invention relates generally to methods and systems for simulatingprogressive lenses, more specifically to simulate progressive lenseswith a guided lens design exploration system.

BACKGROUND

Prescriptions for eyeglasses, or spectacles, are generated today bydevices and methods that were developed many decades ago, theirfoundations going back even farther in time. While these methods andtechnologies are mature, and served the patient population well, theydid not benefit from the remarkable progress of optoelectronics andcomputational methods that revolutionized so many areas intelecommunications, consumer electronics, and interactivefunctionalities. Viewing optometry from the vantage point of modernopto-electronics and computer science, several areas of needs andopportunities can be identified. Some of the leading challenges andopportunities are listed and reviewed below.

(1) No trial before purchase: Patients who are asking for a progressivelens prescription are examined today only with single-power,non-progressive lenses, one for distance vision, and one for nearvision. Therefore, the patients do not experience the prescribedprogressive lenses in their totality before they purchase it. Since thepatients do not “test-drive” the progressive lens prescriptions, theydiscover problems or inconveniences too late, only after the glasseshave been provided to them.

(2) Only analog optometric devices are used: The single power lenses andother optical systems used by optometrists today have been developedlong time ago. They are analog optical systems, and have not adapted andadopted the many advances of modern optoelectronic technology. This isto be contrasted by the very successful adaptation of optoelectronictechnologies into other areas of ophthalmology, such as retinal imagingby Optical Coherence Tomography, aberrometric diagnostic devices, andoptical diagnostic devices used in preparation for cataract surgery todetermine the appropriate intraocular lenses.

(3) Only two distances tested: These analog lens systems test apatient's vision only at two distances, the near and the distancevision. In contrast, most patients have unique usage patterns, and oftenneed the optimization of their spectacles for three or more distancesdepending on their individual habits.

(4) Eyes are only tested individually: Most of the diagnostic methodsare applied for single eyes, blocking the other eye. Such approachesdisregard the coordination between the eyes when they create the visualexperiences, as well as the various effects of the vergence that arequite important for the full evaluation of the binocular visual acuity.

(5) Progressive lens prescriptions are under-defined: The design ofprogressive lenses is a complex process. Different companies havedifferent proprietary optimization algorithms, with many parameters thatuse different search and optimization strategies. In contrast, theoptometrists only determine 2-3 parameters about a patient's visionduring an office visit. In a mathematical sense, providing only 2-3parameters for the design of a progressive lens seriously under-definesthe optimization algorithm of the lens design. When the designoptimization algorithms have insufficient information, the algorithmscan, and often do, stop at designs that are not truly optimal, and theyare unable to identify the truly optimal design.

(6) Determining more parameters would increase treatment time perpatient: Optometrists could run more tests to determine more parameters.However, doing so would extend the time spent with individual patients.This would have a negative effect on the economic model of theoptometrists.

(7) Patients often need to return glasses for adjustments: Patients areunsatisfied with their progressive lenses in a statistically relevantfraction of the cases. Therefore, patients often return to theoptometrist office asking for adjustments. It is not uncommon that aprogressive lens has to be readjusted 3-4-5 times. The time and costassociated with these return visits seriously impacts the satisfactionof the patient, and undermines the economic model of the optometrist.

(8) Lens design verification groups are small: Lens design algorithmsare typically optimized in interaction with a small test group, fromless than a hundred to a couple hundred patients. Using only such smalltest groups for optimizing such a complex problem can lead to lensdesign algorithms that are not optimal. Subsequent complaints from thelarger number of real patients yields feedback from a larger group, butthis feedback is incomplete and uni-directional.

(9) Testing images do not reflect patient's actual visual needs: Eyetesting uses standardized letters that are rarely reflective of apatient's actual needs. Testing just about never involves images thatare relevant for the individual patient.

(10) Peripheral vision rarely tested: Optometrists rarely testsperipheral vision, whereas for some professions, peripheral vision mightbe a high value component of the overall visual acuity.

(11) Modern search algorithms are not yet utilized: Recent advances thatgreatly boost the efficiency of search algorithms over complexmerit-landscapes, have not yet been adapted to the design of progressivelenses.

(12) Artificial intelligence is not used: Recent advances inimplementing Artificial Intelligence for system improvements also havenot found their way yet into optometry.

At least the above dozen problems demonstrate that optometry couldgreatly benefit in a large number of ways from implementing modernoptoelectronic technologies, in a patient-centric, customized manner,that also uses progress in modern computer science.

SUMMARY

To address the above described medical needs, some embodiments of theinvention include a Progressive Lens Simulator, comprising: an EyeTracker, for tracking an eye axis direction to determine a gazedistance, an Off-Axis Progressive Lens Simulator, for generating anOff-Axis progressive lens simulation (Off-Axis PLS); and an AxialPower-Distance Simulator, for simulating a progressive lens power in theeye axis direction, thereby creating a Comprehensive progressive lenssimulation from the Off-Axis PLS.

Embodiments also include a method of operating a Progressive LensSimulator, the method comprising: tracking an eye axis direction by anEye Tracker to determine a gaze distance; generating an off-axisprogressive lens simulation (Off-Axis PLS) by an Off-Axis ProgressiveLens Simulator; and creating a Comprehensive progressive lens simulationfrom the Off-Axis PLS by simulating a progressive lens power in the eyeaxis direction by an Axial Power-Distance Simulator.

Embodiments further include a Progressive Lens Simulator, comprising: anEye Tracker, for tracking an eye axis direction to determine a gazedistance; an Integrated Progressive Lens Simulator, for creating aComprehensive Progressive Lens Simulation (PLS) by simulating aprogressive lens power in the eye axis direction, in combination withgenerating an Off-Axis progressive lens simulation (Off-Axis PLS).

Embodiments also include a Head-mounted Progressive Lens Simulator,comprising: an Eye Tracker, for tracking an eye axis direction todetermine a gaze distance; an Integrated Progressive Lens Simulator, forcreating a Comprehensive Progressive Lens Simulation (PLS) by simulatinga progressive lens power in the eye axis direction, in combination withgenerating an Off-Axis progressive lens simulation (Off-Axis PLS);wherein the Eye Tracker and the Integrated Progressive Lens Simulatorare implemented in a head-mounted display.

Embodiments further include a Guided Lens Design Exploration System forProgressive Lens Simulator, comprising: a Progressive Lens Simulator,for generating a progressive lens simulation for a patient with aprogressive lens design; a Feedback-Control Interface, for inputting atleast one of control and feedback by the patient, in response to theprogressive lens simulation; and a Progressive Lens Design processor,coupled to the Feedback-Control Interface, for receiving the at leastone of control and feedback from the patient, and modifying theprogressive lens design in response to the receiving, wherein theProgressive Lens Simulator is configured to generate a modifiedprogressive lens simulation for the patient with the modifiedprogressive lens design.

Embodiments also include a method of Progressive Lens Simulation,comprising: (a) activating a lens design with a Progressive Lens DesignProcessor; (b) generating an image by an Image Generator of aProgressive Lens Simulator; (c) generating a Comprehensive PLS,simulated from the generated image by the Progressive Lens Simulator,utilizing the lens design; (d) acquiring a visual feedback via aFeedback-Control Interface, responsive to the generating of theComprehensive PLS with the lens design; (e) modifying the lens design bythe Progressive Lens Design Processor in relation to the visualfeedback; and (f) re-generating the Comprehensive PLS with the modifiedlens design by the Progressive Lens Simulator.

Embodiments further include a Deep Learning Method for an ArtificialIntelligence Engine for a Progressive Lens Design Processor, comprising:activating a Visual Feedback-Design Factor Neural Network for aProgressive Lens Design Processor, receiving as input a visual feedbackvector into the Visual Feedback-Design Factor Neural Network; outputtinga design factor vector with the Visual Feedback-Design Factor NeuralNetwork in response to the inputting; wherein coupling matrices of theVisual Feedback-Design Factor Neural Network of the Progressive LensDesign Processor were trained by performing a deep learning cycle.

Embodiments also include a Supervised Multi-station system ofProgressive Lens Simulators, comprising: a set of Progressive LensSimulators, individually including an Eye Tracker, for tracking an eyeaxis direction to determine a gaze distance; an Off-Axis ProgressiveLens Simulator, for generating an Off-Axis progressive lens simulation(Off-Axis PLS); and an Axial Power-Distance Simulator, for simulating aprogressive lens power in the eye axis direction, thereby creating aComprehensive progressive lens simulation from the Off-Axis PLS; and aCentral Supervision Station, in communication with the Progressive LensSimulators, for providing supervision for an operation of the individualProgressive Lens Simulators.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a Guided Lens Design Exploration System of SimulatedProgressive Lenses (GPS).

FIG. 2 illustrates the Guided Lens Design Exploration System ofSimulated Progressive Lenses (GPS) in some detail.

FIG. 3 illustrates a Multistage embodiment of a Progressive LensSimulator.

FIGS. 4A-B illustrate an Off-Axis Progressive Lens Simulator OPS.

FIG. 5 illustrates a Progressive Lens Simulator with a Vergence-DistanceSimulator VDS and a Zoom-Distance Simulator ZDS.

FIGS. 6A-B illustrate embodiments of an Axial Power-Distance SimulatorADS.

FIG. 7 illustrates a Multistage embodiment of a Progressive LensSimulator.

FIG. 8 illustrates a Method to operate a Multistage embodiment of aProgressive Lens Simulator.

FIG. 9 illustrates an Integrated Progressive Lens Simulator.

FIG. 10 illustrates a MEMS Laser Scanner.

FIGS. 11A-B illustrate a MEMS Deformable Mirror, and a MEMS ActuatedMirror Array.

FIGS. 12A-D illustrate a Microlens Array, a MEMS Curved Mirror Array, aLED projector Array, and a Deformable display embodiment of the IPLS.

FIG. 13 illustrates a method of operating an Integrated Progressive LensSimulator.

FIG. 14 illustrates a head-mounted Integrated Progressive LensSimulator.

FIGS. 15A-B illustrate embodiments of Head-mounted IntegratedProgressive Lens Simulators.

FIG. 16 illustrates a Progressive Lens Simulator with a Lens DesignExploration System.

FIGS. 17A-F illustrate embodiments of patient controllers.

FIGS. 18A-B illustrate methods of Progressive Lens Simulation in somedetail.

FIGS. 19A-B illustrate methods of Progressive Lens Simulation in somedetail.

FIGS. 20A-B illustrate Design Factors.

FIGS. 20C-D illustrate Visual Feedbacks.

FIG. 21 illustrates a modifying of a Design Factor based on VisualFeedback in the Design Factor space.

FIGS. 22A-B illustrate a Visual Feedback-to-Design Factor matrix of aVisual Feedback-to-Lens Design Transfer Engine.

FIGS. 23A-B illustrate a Search Management method, with an interactiveaspect.

FIGS. 24A-B illustrate Lens Merit factors.

FIG. 25 illustrates a modifying of a Design Factor based on VisualFeedback and Lens Merit factors in the Design Factor space.

FIG. 26 illustrates a Visual Feedback+ Lens Merit-to-Lens Design matrixof a Visual Feedback-to-Lens Design Transfer Engine.

FIG. 27 illustrates a method for modifying the Design Factor locally.

FIGS. 28A-B illustrate a method for modifying the Design Factornon-locally.

FIGS. 29A-B illustrate performing a Search Management step, in somecases interactively.

FIG. 30 illustrates a Progressive Lens Simulator with a Guided LensDesign Exploration System and with Artificial Intelligence Engines.

FIG. 31 illustrates a Visual Feedback-Design Factor Neural Network.

FIG. 32 illustrates an Artificial Intelligence Engine for ProgressiveLens Design Processor.

FIG. 33 illustrates a Backpropagation with Gradient Descent.

FIG. 34 illustrates a Deep Learning method for an AI Engine for aProgressive Lens Design Processor.

FIG. 35 illustrates a Deep Learning method for an AI Engine for a SearchGuidance Engine.

FIG. 36 illustrates a Supervised Multi-station System of ProgressiveLens Simulators.

DETAILED DESCRIPTION

The systems and methods described in the present patent document addressthe above articulated medical needs at least in the following aspects.These aspects are organized in a contrasting format to the previouslydescribed challenges of the state of the art.

(1) No trial before purchase: In embodiments, the visual experience ofprogressive lenses is simulated by a Progressive Lens Simulator. Thissystem empowers the patient to actively and interactively explore andexperience progressive lenses with different designs in real time. Thepatient can explore as many simulated progressive lens designs as she orhe wishes before settling on a particular design. In short, the patientcan explore, “test drive”, and “try on” the progressive lens beforepurchasing it.

(2) Only analog optometric devices are used: Embodiments use moderndigital optoelectronic technology instead of analog opticaltechnologies.

(3) Only two distances tested: The patient can explore the performanceof the various progressive lens designs at as many distances as he orshe wishes by virtue of modern optoelectronic designs.

(4) Eyes are tested individually: The patient can explore the visualexperience with the simulated progressive lenses with both eyessimultaneously. This approach allows the lens design selection processto include and optimize for the patient's experience of the effects ofvergence.

(5) Progressive lens prescriptions are under-defined: The patient canexplore the many possible progressive lens designs exhaustively. Thesearch can concentrate on many particular aspects of the performance ofthe lens. Monitoring the search process in detail provides an extensiveamount of data about the patient's vision for the advanced lens designsoftware. The acquisition of this larger amount of data turns the lensdesign process from under-defined to appropriately defined in amathematical sense. The exploration process can be continued until thelens design software concludes that it has sufficient data to zoom in onthe most optimal lens design.

(6) Determining more parameters would increase treatment time perpatient: The selection process of the best progressive lens design withthe here-described embodiments may take longer, or even substantiallylonger than the duration of a typical office visit today. This may beperceived as an economic “excessively high costs” argument against thedescribed system. However, much of the search is guided by smartsoftware and thus does not require the active presence of theoptometrist. Rather, the optometrists play a supervisory role and thuscan supervise even a greater number of patients per day with theseProgressive Lens Simulators than with the traditional methods.

(7) Patients often need to return glasses for adjustments: Since thepatients explore all relevant and possible progressive lens designs inreal time, the here-described Progressive Lens Simulator systemminimizes the patient complaints and returns. This dramatically improvespatient satisfaction and greatly boosts the economic model of theoptometrists.

(8) Lens design verification groups are small: The search data arecollected from all participating optometrist offices. Therefore, theprogressive lens design software will have access to the recorded searchpatterns, patient behaviors, and eventual patient choices from the testgroup of millions of patients. Access to such a huge and rapidly growingdatabase will be used to rapidly and efficiently improve the progressivelens design algorithms.

(9) Testing images do not reflect patient's actual visual needs: TheProgressive Lens Simulators offer to patients the testing of theirvision on any images they choose. A promising implementation ispresenting use-relevant images that are relevant for and simulate thepatient's actual activities.

(10) Peripheral vision rarely tested: The digital image-projectionsystem can present both central and peripheral images for a fullcharacterization of a patient's vision.

(11) Modern search algorithms are not yet utilized: The Guidance Systemfor Patient Exploration in some embodiments uses modern searchtechniques that were developed to explore complex, multiplyinterdependent and constrained merit-landscapes.

(12) Artificial Intelligence is not used: Artificial IntelligenceEngines are used for constantly improving and upgrading the system'ssoftware block-by-block.

The here-described systems can only yield positive outcomes, becausethey can be first used to determine the traditional progressive lensprescription with the traditional procedure. Subsequently, theProgressive Lens Simulator can perform various advanced searches byhere-described embodiments, and guide the patient to the advancedprogressive lens prescription best suited for him/her. Finally, theProgressive Lens Simulator can simulate the traditional progressive lensexperience, followed by the advanced progressive lens experience, andalternate back-and-forth so that the patient can compare the twoprogressive lens experiences to make her/his final choice. Since thepatient can always return to and select the traditional progressive lensdesign, the overall outcome of the process cannot be worse than thetraditional process, only better.

This patent document describes many embodiments, techniques and methods.It also describes more than a dozen advantages over existing traditionalsystems. Thus, the described advantages are not limiting for allembodiments. Indeed, embodiments which possess only one or a few of theadvantages are already novel over existing systems. Also, several other,not-yet-listed advantages exist that make the system novel. Moreover,several of the described aspects can be combined in various embodimentsfor additional synergetic advantages.

FIG. 1 and FIG. 2 illustrate the Guided Lens Design Exploration Systemof Simulated Progressive Lenses (GPS) 10 on a high, system level.Embodiments of the GPS 10 possess one or more of the above describedfeatures and advantages, as follows.

(1) Patients can explore and “try on” many different progressive lensdesigns before selecting one for purchase.

(2) Embodiments use modern digital optoelectronic technology.

(3) Patients can test their vision at as many distances as they wish.

(4) The two eyes of the patients can be tested synchronously, thusfactoring their vergence into the overall visual experience.

(5) The eventually selected progressive lens designs and prescriptionsare well-defined because a sufficient number of parameters aredetermined.

(6) Since patients are exploring the progressive lens designs on theirown, guided by a smart software, the demand on the time of theoptometrists actually decreases relative to present systems, as theoptometrists are expected only to supervise the patients' exploration.

(7) Since patients select their own design, glasses are returned foradjustments much less frequently.

(8) Lens design verification groups are large and ever expanding.

(9) Testing images reflect patient's actual visual needs.

(10) Patients' peripheral vision is tested as extensively as thepatients desire.

(11) Cutting edge search algorithms are utilized to guide the patientexploration.

(12) Artificial Intelligence is used to continuously upgrade and improveboth the lens design and the patient guidance systems.

These system level concepts of the GPS 10 are described in general inFIGS. 1-2, and subsequently in detail in FIGS. 3-36. In particular,FIGS. 3-15 describe many embodiments of Progressive Lens Simulators thatgenerate life-like Comprehensive Progressive Lens Simulations of as manyprogressive lens designs as the patient wishes to explore. FIGS. 16-29describe guidance systems and methods that guide the patients in theirexploration of the progressive lens designs. FIGS. 30-35 describeArtificial Intelligence systems and methods to train and improve theProgressive Lens Simulators. Finally, FIG. 36 describes a supervisedMulti-station GPS system.

FIG. 1 illustrates that the Guided Lens Design Exploration System ofSimulated Progressive Lenses (GPS) 10 can include a Progressive LensSimulator (PLS) 100, for simulating various progressive lens designs, aLens Design Exploration System for Progressive Lens Simulator (LDES)300, for intelligently guiding the exploration of the many possibleprogressive lens designs by the patient; and an Artificial IntelligenceEngine for GPS (AI-GPS) 500, for monitoring the lens design explorationprocess by the patients, in order to discover and extract possibleimprovements of the GPS 10 system, followed by actually implementing ofthe discovered improvements. These three major building blocks of thefully integrated GPS 10 system can be all coupled to each other forefficient communication. [In the rest of the specification, sometimesonly the abbreviating acronyms will be used for reference and brevity.]

FIG. 2 illustrates elements of these three main building blocks PLS 100,LDES 300 and AI-GPS 500 in some detail. The Progressive Lens SimulatorPLS 100 can include an Eye Tracker (ET) 110, for tracking the directionof an axis, or gaze, of the patient's eyes, as well as the eyemovements. The Eye Tracker 110 can determine the distance of the targetthe patient is looking at from the vergence of the axes of the two eyes.Several Eye Tracker designs are known in the art and can be adapted andimplemented in this PLS 100. The PLS 100 can further include an Off-AxisProgressive Lens Simulator (OPS) 120, for simulating the off-axis visualexperience of a selected Progressive Lens design. This experience isquite complex, as the effective optical power of progressive lenseschanges with the angle relative to the optical axis.

The PLS 100 can further include an Axial Power-Distance Simulator (ADS)130, that simulates a combination of the distance of the viewed imageand the axial power of the progressive lens. Since the PLS 100 simulatesprogressive lenses, the optical power varies substantially over the lenssurface. In multistage embodiments, the PLS 100 simulates this with acombination of simulating the most important axial power with the ADS130, and the off-axis power with the separate OPS 120. In integratedembodiments, the PLS 100 simulates the spatially varying optical powerwith an Integrated Progressive Lens Simulator IPLS 200.

The PLS 100 can further include a Vergence-Distance Simulator (VDS) 140that simulates the viewing distance in a different manner. The VDS 140can present the images for the two eyes not dead ahead, but moved closerto each other, in order to create the visual experience of the targetimage being at a closer viewing distance. Finally, a Zoom-DistanceSimulator (ZDS) 150 can simulate a change of the viewing distance(changed by the PLS 100 from a first distance to a second distance) byzooming the image in or out. Doing so can further increase the sense ofreality of the visual experiences generated by the PLS 100 for thepatients.

The GPS 10 can include the Guided Lens Design Exploration System forProgressive Lens Simulator (LDES) 300, to guide the exploration of thelarge number of possible progressive lens designs by the patient with anefficient and informed strategy. The LDES 300 can include aFeedback-Control Interface (FCI) 310. This FCI 310 can be used by thepatient to enter feedback and control signals for the PLS 100, in orderto express preferences and provide feedback on the simulated progressivelens designs 123. In some embodiments, the LDES 300 can include aProgressive Lens Design Processor (PLD) 320. The PLD 320 can create thespecific progressive lens design based on measurements of the patient'seyes; based on the patient's input, feedback, and control signals, andon lens design algorithms. The created specific progressive lens designscan be communicated by the PLD 320 to the PLS 100 to create thecorresponding progressive lens visual experience for the patient.

The LDES 300 can further include a Search Guidance Engine (SGE) 330. Thepatient often, or even typically may not know how to change the designof the progressive lens to improve its optical performance. The patienttypically only senses that the last change of the design made the visualexperience better or worse. Or, the patient can articulate whatimprovements she/he is looking for. But since the progressive lensdesign can be modified in many different ways to bring about suchchanges, a guidance system to affect the desired change in an informedand strategic manned can be useful and in fact necessary. Providing suchguidance is one of the functions of the SGE 330. The SGE 330 can receivea desired improvement or preference from a patient, and then suggest inreturn to the patient how to translate the requested improvement into achange of the lens design.

Some embodiments of the LDES 300 can further include a Synchronous EyeExploration Controller (SEC) 340, that oversees and controls the visualexperience of the two eyes synchronously, and plays an important role inintegrating desired design improvements from the two eyes. Finally, theLDES 300 can also include a Peripheral Vision Explorer (PVE) 350, thatevaluates the patient's vision in the peripheral zones, and feeds backthis information into the simulation of the progressive lenses by thePLS 100.

Finally, the Artificial Intelligence Engine for GPS (AI-GPS) 500 can beincluded into some embodiments of the GPS 10, for monitoring theperformance of the components of the PLS 100 and the LDES 300, and fordeveloping suggested adjustments to improve the performance of themanaged components of the GPS system 10. In some detail, the GPS 10 caninclude an Artificial Intelligence (AI) Engine for the Progressive LensDesign Processor (AI-PLD) 510, to monitor and improve the performance ofthe PLD 320. Other embodiments can include an AI engine for the SearchGuidance Engine (AI-SGE) 520. Finally, some embodiments of the GPS 10can include an AI Engine for the Progressive Lens Simulator (AI-PLS)530. Each of the three AI engines 510/520/530 can be configured tomonitor the functioning of the corresponding system blocks PLD 320, SGE330, and PLS 100, and then perform AI-based training cycles to improvethe performance of these blocks. In some embodiments, these AI enginesare implemented by neural networks.

The above, system-level description of the GPS 10 is now expanded withthe detailed description of a number of specific embodiments in moredetail. For clarity, the presentation of these embodiments is organizedinto titled sections.

1. Progressive Lens Simulator with an Axial Power-Distance Simulator

FIG. 3 illustrates a Multistage embodiment of a Progressive LensSimulator PLS 100, comprising: an Eye Tracker (ET) 110, for tracking aneye axis direction to determine a gaze distance, an Off-Axis ProgressiveLens Simulator (OPS) 120, for generating an Off-Axis progressive lenssimulation (Off-Axis PLS) 50 according to a progressive lens design 123;and an Axial Power-Distance Simulator (ADS) 130, for simulating aprogressive lens power in the eye axis direction, thereby creating aComprehensive progressive lens simulation 30 from the Off-Axis PLS 50.The eye axis direction is sometimes referred to as a visual axis.

In some embodiments, the Off-Axis Progressive Lens Simulator OPS 120 caninclude an Image Generator 121, for generating an image 21; an Off-AxisProgressive Lens Simulator Processor, or OPS processor, 122, fortransforming the generated image 21 into Off-Axis PLS signals 20-1 and20-2 according to according to the progressive lens design 123; andOff-Axis Progressive Lens Simulator Displays 124-1/124-2, for displayingthe Off-Axis Progressive Lens Simulation (Off-Axis PLS) 50 according tothe Off-Axis PLS signal 20. Here and in the following, many items X areincluded in the GPS 10 pairwise, one for each eye. They will betypically labeled as items X-1 and X-2. Sometimes, for brevity, thecollection of items X-1 and X-2 will be simply referred to as X.

In some PLS 100, the Off-Axis Progressive Lens Simulator Display 124includes a pair of Off-Axis Progressive Lens Simulator Screens 124-1 and124-2, each displaying an Off-Axis PLS 50-1 and 50-2, the two PLStogether providing a stereoscopic Off-Axis PLS 50 for a first/left eye 1and a second/right eye 2.

In some PLS 100, the Off-Axis Progressive Lens Simulator Display 124includes a single stereoscopic alternating Off-Axis Progressive LensSimulator Screen 124, controlled by an image-alternator, for alternatingthe displaying the Off-Axis PLS 50-1 for the first eye 1, andsubsequently PLS 50-2 for the second eye 2, with suitable stereoscopicadjustments. This rapidly alternating display on left eye/right eyeimages, with synchronized image-alternation, i.e. blocking the image forthe non-targeted eye, allows the use of a single screen to generatestereoscopic images and viewing experience. This image-alternatingtechnology has mechanical embodiments involving shuttering or rotatingwheels, opto-electronic embodiments involving rapid polarizationchanges, and liquid crystal embodiments to block the images. Any one ofthese embodiments can be utilized in the stereoscopic alternatingOff-Axis Progressive Lens Simulator Screen 124.

FIG. 4A illustrates that the progressive lens design 123 includescharacteristic regions of a typical progressive lens. These include: adistance vision region 123 d in the upper portion of the progressivelens with a distance vision optical power OPd, a near vision region 123n in the lower portion of the progressive lens, typically nasallyshifted, having a stronger, near vision optical power OPn, and aprogression region 123 p, sometimes also called a channel, where theprogressive optical power OPp progressively and smoothly interpolatesbetween OPd and OPn. The progressive lens design 123 also includes atransition region 123 t, typically on both sides of thechannel/progression region 123 p, where the front and back surfaces ofthe lens are shaped to minimize optical distortions arising from theoptical power progressing in the channel 123 p.

FIG. 4A illustrates that in the Progressive Lens Simulator PLS 100, theOff-Axis Progressive Lens Simulator Processor 122 can be configured (1)to receive the generated image 21 from the Image Generator 121; and (2)to transform the generated image 21 into the Off-Axis PLS signal 20 byintroducing a locally varying blur 126, representative of theprogressive lens design 123. This blur 126 is caused by the opticalpower of the progressive lens design locally varying in the transitionregion 123 t and in the channel, or progressive region 123 p, causingthe light rays from an object point not getting focused into a single,well-defined image point.

Analogously, in some embodiments of the PLS 100, the OPS Processor 122can be configured (1) to receive the generated image 21 from the ImageGenerator 121; and (2) to transform the generated image 21 into theOff-Axis PLS signal 20 by introducing a locally varying curvature, orswim 127, representative of the progressive lens design 123.

FIG. 4B illustrates the swim 127 with a square grid as the imagedobject. A typical progressive lens design 123 bends and curves theoriginally rectilinear lines of the square grid into a curved swim grid128. These two effects are demonstrated in FIG. 4A: the transitionregions of a regular image develop the blur 126 by the typicalprogressive lens design 123, and the straight lines get bent by the swim127.

The OPS Processor 122 can perform a detailed ray tracing computation oflight emanating from the generated image 21 through the progressive lensdesign 123 to quantitatively produce this blur 126 and swim 127. Inalternate embodiments, wavefront propagation methods can be used by theOPS Processor 122. Generating the correct blur 126 and swim 127 are keyfunctions of the OPS Processor 122 to generate the life-like Off-AxisProgressive Lens Simulation (PLS) 50 at least because of the followings.When a patient evaluates the visual experience of the particularprogressive lens design 123, the primary positive experience is thecustomized increase of the optical power from OPd in the distance regionto OPn in the near region, while the primary negative experience is “theprice of the positives”, the concomitant blur 126 and swim 127, inducedby the power progression. The GPS 10 simulates different progressivelens designs 123 for a patient. The search for the optimal progressivelens design 123 is performed by the patient evaluating the balance ofthe positives against the negatives of the Comprehensive ProgressiveLens Simulations 30 of the individual simulated designs 123, eventuallyidentifying his/her most preferred design 123. The OPS Processor 122crucially helps this search process by the most life-like simulation ofthe blur 126 and swim 127 of the designs 123. In some PLS 100, at leasttwo of the Image Generator 121, the Off-Axis Progressive Lens SimulatorProcessor 122, and the Off-Axis Progressive Lens Simulator Display 124can be integrated.

FIG. 3 and FIG. 5 further illustrate that the PLS 100 can include aVergence-Distance Simulator VDS 140, for simulating a vergence for thedisplayed Off-Axis PLS 50 at the gaze distance, as determined by eitherthe Eye Tracker 110, or intended by an operator. The utility of the VDS140 was outlined earlier. The life-like visual experience of theComprehensive PLS 30 can be further enhanced by moving the Off-AxisProgressive Lens Simulations PLS 50-1 and 50-2, and thereby theComprehensive PLS 30-1 and 30-2 closer to each other when the eyes focuson a closer gaze distance. This can happen when the patient decides tolower and inwardly rotate her/his visual axis, intending to look throughthe near vision region 123 n of the simulated progressive lens design123. Another situation is when an operator, or a computer controller ofGPS 10, decides to present a Comprehensive PLS 30 that corresponds to acloser gaze distance, to test the near vision of a patient. Simulatingthe vergence corresponding to a gaze distance correctly enhances thelife-like visual experience to a remarkable degree.

The Vergence-Distance Simulator VDS 140 can be configured to simulatethe vergence for the displayed Off-Axis PLS 50 at the gaze distance by(1) moving a screen of the Off-Axis Progressive Lens Simulator Display124 dominantly laterally, or by (2) shifting the displayed Off-Axis PLS50 on the Off-Axis Progressive Lens Simulator Display 124 dominantlylaterally. In the latter embodiment, the Off-Axis PLS 50 is typicallydisplayed only on a portion of the OPS display 124, thus leaving room toelectronically moving the image of the Off-Axis PLS 50 laterally. SomePLS 100 includes the combination of (1) and (2). Other solutions can beused as well, such as rotating mirrors in the optical path of theOff-Axis PLS 50.

FIG. 5 illustrates that in some embodiments of the PLS 100, the VDS 140can include a VDS processor 142, optionally coupled to the OPS Processor122 to receive a vergence signal 40 and to control the VergenceSimulation. The VDS processor 142 can be coupled to vergence VDSactuators 144-1 and 144-2. In some embodiments, these VDS actuators144-1 and 144-2 can mechanically move the OPS displays 124-1 and 124-2laterally.

FIG. 3 and FIG. 5 also illustrate that some PLS 100 can include aZoom-Distance Simulator ZDS 150, to further increase the life-likevisual experience of the Comprehensive PLS 30 by zooming theComprehensive PLS 30 in accord with the changes of the gaze distance.This ZDS 150 can be activated when a patient decides to move his/hergaze relative to the progressive lens design 123. For example, thepatient moves his/her gaze from the distance vision region 123 d to thenear visions region 123 n of the progressive lens design, in order tolook at a near object. The ZDS 150 can increase the life-like experienceof this move by zooming in on the near object. As shown in FIG. 5, thePLS 100 can include a ZDS processor 152, coupled to the OPS Processor122 to receive or send a zoom signal 50. In some cases, the ZDSprocessor 152 can be notified by the Eye Tracker 110 that the patientturned his/her gaze direction lower and inward, as part of a process ofswitching to looking at a nearby portion of the overall generated image21, for example to look at a foreground object. In response, the ZDSprocessor 152 can notify the OPS Processor 122 via the zoom signal 50 tozoom in on the nearby portion of the generated image 21, for example, onthe foreground object.

With modern opto-electronic techniques, the above described simulatorscan be integrated to various degrees. In some PLS 100, at least one ofthe Off-Axis Progressive Lens Simulator Processor 122, the Off-AxisProgressive Lens Simulator Display 124, and the Axial Power-DistanceSimulator ADS 130 can include at least one of the Vergence-DistanceSimulator 140 and the Zoom-Distance Simulator 150. In some cases, onlythe VDS processor 142 or the ZDS processor 152 can be included.

Next, the description turns to various embodiments of the AxialPower-Distance Simulator ADS 130. In general, the ADS 130 can be anadjustable optical system that has an adjustable optical refractivepower. This adjustable optical refractive power of the ADS 130 can beadjustable to be consistent with the gaze distance, determined by theEye Tracker 110. In other embodiments, the adjustable optical power ofthe ADS 130 can be adjusted to an intended gaze distance, such as whenthe patient, or an operator of the GPS 10 decides to explore and testvision at a different distance.

In some embodiments, the ADS 130 uses optical elements such as lensesand mirrors whose optical power is adjustable, but whose position isfixed. In other embodiments, the ADS 130 can use optical elements whoseposition is also adjustable to simulate a vergence corresponding to theeye axis direction, or visual axis. A simple embodiment of the ADS caninclude a pair of adjustable lenses or mirrors that are laterallytranslatable, or rotatable, to increase the life-likeness of thesimulation of the vergence.

FIGS. 6A-B illustrate specific embodiments of ADS 130. FIG. 6Aillustrates an ADS 130 that includes an Alvarez lens system 132. TheAlvarez lens system 132 can include at least two (sliding) lenses 134-1and 134-2 for each eye, at least one of the two lenses 134 havinglaterally varying curvature; and one or more actuators 135, for slidingat least one of the lenses 134 laterally relative to the other lens,thereby changing an optical refractive power of the Alvarez lens system132 in a central region. The actuator 135 is only shown once to avoidclutter. In embodiments of the Alvarez lens system 132 the optical(refractive) power in a central region can be changed by 2 Diopters (2D)or more, without introducing substantial aberrations. The diameter ofthe central region can be 2, 2.5, 3 cm, or more than 3 cm. Adding 2Doptical power to the ADS 130 changes the perceived image distance fromfar away to ½D=50 cm. Therefore, the ability to change the optical powerby 2D is typically sufficient to change the axial optical power from OPdof the distance vision region 123 d to OPn of the near vision region 123n, thereby simulating the entire range of interest. As described before,one function of the ADS 130 is to simulate the gaze distance to theobject, the other function is to simulate the axial optical power of theprogressive lens design 123. Different ADS 130 can integrate andimplement these two functions differently.

FIG. 6B illustrates another embodiment of the ADS 130: an adjustablefluid lens system 136 that includes a pair of adjustable fluid lenses138-1, with optical refractive power that is controlled by an amount offluid in the fluid lenses 138-1 (only one lens shown); afluid-management system 138-2, for adjusting the amount of fluid in thefluid lenses 138-1; and lens-adjusting electronics 138-3, forcontrolling the pair of adjustable fluid lenses 138-1 and thefluid-management system 138-2 to adjust the optical refractive power ofthe ADS 130. As shown, the adjustable fluid lens 138-1 can include adeformable rounded polymer skin that contains a liquid. The fluidmanagement system 138-2 can inject or drain fluid from the lens 138-1.Doing so changes a center height from h₁ to h₂. By the basic law oflenses, this height change changes the focal length of the lens 138-1from f₁ to f₂, as shown, adjusting its optical refractive power.

Many other embodiments of the ADS 130 exist, including a shape-changinglens, an index-of-refraction-changing lens, a variable mirror, adeformable optic, an aspheric lens with adjustable aperture, a liquidcrystal optical element, an adjustable reflective optical element, anadjustable opto-electronic element, and an optical system with anoptical component with adjustable relative position.

FIG. 7 illustrates an embodiment of the PLS 100 in more detail. Most ofthe elements of the PLS 100 of FIG. 7 are specific embodiments of thegeneral PLS 100 of FIG. 3, and will not be repeated here. Next,additional features of FIG. 7 are called out as follows.

In the PLS 100 of FIG. 7, the Eye Tracker 110 can include infrared lightemitting diodes, or IR LEDs, 112-1 and 112-2, positioned close to afront of the PLS 100, to project infrared eye-tracking beams on thefirst eye 1 and the second eye 2; as well as infrared light sources111-1 and 111-2, to illuminate the first eye 1 and the second eye 2 withan infrared imaging light. The infrared eye-tracking beams and theinfrared imaging light get both reflected from the eyes 1 and 2, asreflected IR beam and IR imaging light 11-1 and 11-2.

The Eye Tracker 110 can further include infrared (IR) telescopes 113-1and 113-2, with infrared (IR) cameras 114-1 and 114-2, to detect theinfrared eye-tracking beams and the infrared imaging light 11-1 and11-2, reflected from the eyes 1 and 2. The IR cameras 114-1 and 114-2then generate the eye-tracking images 14-1 and 14-2, and send them tothe eye-tracking processor 115. The eye-tracking processor 115 canprocess and analyze these eye-tracking images to generate eye-trackingimage/data 15-1 and 15-2, or jointly 15. In some detail, the IR beams ofthe IR LEDs 112 are reflected as Purkinje reflections, or Purkinjespots, that reflect from the various surfaces of the eye, starting withthe cornea. Tracking these Purkinje spots delivers pin-point informationto track the eye position and orientation. The IR light source 111, onthe other band, generates a wide-angle IR light that can be used toimage the entire frontal region of the cornea. The Eye Tracker 110 canuse both the pin-point information from the reflected Purkinje spots,and the wide-angle image from the reflected imaging light (togetherreferenced as 11) to develop the comprehensive eye-tracking image/data15.

In the embodiment of FIG. 3 the Eye Tracker 110 directly sendseye-tracking image/data 15 into the OPS Processor 122. In the embodimentof FIG. 7, there is an intervening eye-tracking processor 115, separatefrom the OPS Processor 122. Several analogous variations have beencontemplated for the various embodiments of PLS 100.

In operation, the OPS Processor 122 can receive the generated image 21from the image generator 121, adjust it to generate the Off-AxisProgressive Lens Simulation signals 20-1 and 20-2, and send theseOff-Axis PLS signals 20 to the OPS Displays 124-1 and 124-2, so that theOPS Displays 124 generate the Off-Axis PLS 50-1 and 50-2.

As shown, in some embodiments, the VDS processor 142 and the ZDSprocessor 152 can be integrated in the OPS Processor 122. In theseembodiments, the Off-Axis PLS signal 20 also includes vergence and zoomcomponents. The vergence component can instruct the VDS actuators 144-1and 144-2 to laterally move, or rotate, the OPS Displays 124-1 and124-2, in order to simulate the needed vergence. In these embodiments,the Off-Axis PLS 50 includes Vergence and Zoom, as indicated.

FIG. 7 illustrates that the PLS 100 can further includeinfrared-transmissive visible mirrors 146-1 and 146-2, one for each eye,to redirect the Off-Axis PLS 50-1 and 50-2, from the OPS display 124-1and 124-2 to the eyes 1 and 2. With this reflection, the Off-Axis PLS50-1 and 50-2 are redirected into the main optical pathway of the PLS100, in the direction of the eyes 1 and 2. The Off-Axis PLS 50-1 and50-2 are finally going through the Axial Power-Distance Simulator ADS130-1 and 130-2. In this PLS, the ADS 130-1 and 130-2 include adjustableoptical power systems 131-1 and 131-2, that can be an Alvarez lenssystem 132, an adjustable fluid lens system 136, or any of the otheradjustable optical elements, described earlier. The ADS 130 transformthe Off-Axis PLS 50 into the Comprehensive Progressive Lens SimulationPLS 30, for the patient's eyes.

It is noted that the infrared-transmissive visible mirrors 146-1 and146-2 reflect visible light, while transmitting infrared light.Therefore, mirrors 146 are configured to reflect the Off-Axis PLS 50towards the eyes, while transmitting the reflected infrared eye trackingbeam and the infrared imaging light 11-1 and 11-2, from the eyes.

In the described embodiment of the PLS 100, the OPS Display screens124-1 and 124-2 can be positioned peripheral to the main optical pathwayof the PLS 100, while the infrared telescopes 113-1 and 113-2 of the EyeTracker 110 can be positioned in the main optical pathway, as shown. Inother embodiments, the positioned can be reversed. The mirrors 146 canbe IR reflective and visible transmissive, in which case the IRtelescopes 113 can be positioned peripherally, while the OPS Displays124 can be positioned in the main optical pathway, in effect tradingplaces.

2. Method of Operating a Progressive Lens Simulator with an AxialPower-Distance Simulator

FIG. 8 illustrates a method 101 m of operating a multistage embodimentof the Progressive Lens Simulator PLS 100. Here the label “m” refers tothe multistage embodiment of the PLS 100. The method 101 m can includethe following steps.

(a) tracking 102 m of an eye axis direction by an Eye Tracker 110 todetermine a gaze distance of the eye;

(b) generating 103 m an Off-Axis Progressive Lens Simulation (Off-AxisPLS) 50 by an Off-Axis Progressive Lens Simulator OPS 120, includingblur and swim, according to a progressive lens design 123;

(c) creating 104 m a Comprehensive Progressive Lens Simulation(Comprehensive PLS) 30 from the Off-Axis PLS 50 by simulating aprogressive lens power in the eye axis direction by an AxialPower-Distance Simulator ADS 130;

(d) shifting 105 m the Off-Axis PLS 50 by a Vergence-Distance SimulatorVDS 140 to vergence appropriate for the gaze distance; and

(e) zooming the Off-Axis PLS 50 by a Zoom-Distance Simulator ZDS 150 tosimulate transitions of gaze distance.

Various aspects of these steps have been described before in relation tothe PLS 100 embodiments of FIGS. 1-7.

The generating 103 m of an Off-Axis PLS 50 can include the following.

(a) generating an image 21 by an Image Generator 121;

(b) transforming the generated image 21 into an Off-Axis PLS signal 20by an Off-Axis Progressive Lens Simulator Processor 122 according to theprogressive lens design 123; and

(c) displaying an Off-Axis PLS 50 according to the Off-Axis PLS signal20 by an Off-Axis Progressive Lens Simulator Display 124.

Various aspects of these steps have been described before in relation tothe PLS 100 embodiments of FIGS. 1-7.

As described earlier, the displaying can include providing astereoscopic Off-Axis PLS 50-1 and 50-2 for the first eye 1 and thesecond eye 2 by a pair of Off-Axis Progressive Lens Simulator Displays,or Screens 124-1 and 124-2.

In other embodiments, the displaying can include alternating thedisplaying the Off-Axis PLSs 50-1 and 50-2 for the first eye 1, andsubsequently for the second eye 2, with suitable stereoscopicadjustments, by a stereoscopic alternating Off-Axis Progressive LensSimulator Screen 124, that is controlled by an image-alternator.

In some embodiments, as shown in FIGS. 4A-B, the transforming caninclude receiving the generated image 21 from the Image Generator 121 bythe Off-Axis Progressive Lens Simulator Processor 122; and transformingthe generated image 21 into the Off-Axis PLS signal 20 by introducing alocally varying blur 126, representative of the progressive lens design123.

In some embodiments, as shown in FIGS. 4A-B, the transforming caninclude receiving the generated image 21 from the Image Generator 121 bythe Off-Axis Progressive Lens Simulator Processor 122; and transformingthe generated image 21 into the Off-Axis PLS signal 20 by introducing alocally varying curvature, or swim, 127, representative of theprogressive lens design 123.

In some embodiments, at least two of the Image Generator 121, theOff-Axis Progressive Lens Simulator Processor 122, and the Off-AxisProgressive Lens Simulator Display 124 can be integrated.

In some embodiments, as shown in FIG. 5, the method 101 m can furtherinclude shifting 105 m the Off-Axis PLS 50 by a Vergence-DistanceSimulator VDS 140 to a vergence appropriate for the gaze distance.

In some embodiments, the simulating a vergence can include moving ascreen of the Off-Axis Progressive Lens Simulator Display 124 dominantlylaterally; and shifting the displayed Off-Axis PLS 50 on the Off-AxisProgressive Lens Simulator Display 124 dominantly laterally.

In some embodiments, as shown in FIG. 5, the method 101 m can furtherinclude zooming 106 m the Off-Axis PLS 50 by a Zoom-Distance Simulator150 to simulate transitions of the gaze distance.

In some embodiments, at least one of the Off-Axis Progressive LensSimulator Processor 122, the Off-Axis Progressive Lens Simulator Display124, and the Axial Power-Distance Simulator ADS 130 can include at leastone of the Vergence-Distance Simulator VDS 140 and the Zoom-DistanceSimulator ZDS 150.

In some embodiments, the simulating a progressive lens power (within thecreating 104 m) can include adjusting an optical refractive power of anadjustable optical power system 131 of the Axial Power-DistanceSimulator ADS 130.

In some embodiments, the adjusting can include adjusting the opticalrefractive power of the Axial Power-Distance Simulator ADS 130 to beconsistent with the determined gaze distance. In some embodiments, theadjusting can include adjusting the Axial Power-Distance Simulator 130to simulate a vergence corresponding to the eye axis direction.

In some embodiments, as shown in FIG. 6A, the adjustable optical powersystem 131 of the Axial Power-Distance Simulator ADS 130 can include anAlvarez lens system 132 that includes at least two lenses for an eye134-1 and 134-2, at least one of the two lenses having laterally varyingcurvature, and one or more actuators 135. In these embodiment, theadjusting can include sliding at least one of the lenses 134-1 laterallyrelative to the other lens 134-2 by the one or more actuators 135,thereby changing an optical refractive power of the Alvarez lens system132 in a central region.

In some embodiments, as shown in FIG. 6B, the adjustable optical powersystem 131 of the Axial Power-Distance Simulator ADS 130 can include anadjustable fluid lens system 136 that includes a pair of adjustablefluid lenses 138-1, with refractive power that is controlled by anamount of fluid in the fluid lenses; a fluid management system 138-2,for adjusting the amount of fluid in the fluid lenses; and lensadjusting electronics 138-3, for controlling the pair of adjustablefluid lenses 138-1 and the fluid management system 138-2. In theseembodiments, the adjusting can include adjusting the amount of fluid inthe fluid lenses 138-1 by the fluid management system 138-2 under thecontrol of the lens adjusting electronics 138-3, thereby changing theoptical refractive power of the adjustable optical power system 131. Insome embodiments, the adjustable optical power system 131 can include ashape-changing lens, an index-of-refraction-changing lens, a variablemirror, a deformable optic, an aspheric lens with adjustable aperture, aliquid crystal optical element, an adjustable reflective opticalelement, an adjustable opto-electronic element, and an optical systemwith an optical component with adjustable relative position. As before,aspects and elements of this method 101 m have been described earlier inrelation to FIGS. 1-7.

3. Integrated Progressive Lens Simulator

In this section, another embodiment of the Progressive Lens Simulator100 will be described. Numerous elements of this embodiment have beenalready described in relation to FIGS. 1-8 and will not be repeated,only referred to where needed.

FIG. 9 illustrates a Progressive Lens Simulator 100 that includes an EyeTracker 110, for tracking an eye axis direction to determine a gazedistance; an Integrated Progressive Lens Simulator (IPLS) 200, forcreating a Comprehensive Progressive Lens Simulation (Comprehensive PLS)30 according to a progressive lens design 123 by simulating aprogressive lens power in the eye axis direction, in combination withgenerating an Off-Axis progressive lens simulation (Off-Axis PLS) 50. Ona general level, embodiments of the IPLS 200 can perform some, or all,of the functions of some, or all, of the OPS 120, the ADS 130, the VDS140 and the ZDS 150 of the previously described multistage PLS 100, eachreferenced with a representative symbol.

In some embodiments, the PLS 100 can include an Image Generator 121, forgenerating an image 21; and a Progressive Lens Simulator Processor 122,for transforming the generated image 21 into a Comprehensive PLS signal20-1 and 20-2 according to the progressive lens design 123, and forcoupling the generated PLS signal 20-1 and 20-2 into the IntegratedProgressive Lens Simulator 200 for creating the Comprehensive PLS 30-1and 30-2. In some embodiments, the Image Generator 121 and theProgressive Lens Simulator Processor 122 can be integrated into the IPLS200.

Just as the multistage PLS 100 could include a single OPS display 124,or a pair of OPS displays 124-1 and 124-2, the PLS 100 of FIG. 9 canalso include a single IPLS 200, or a pair of IPLS 200-1 and 200-2, asshown, for providing a stereoscopic Comprehensive PLS 30-1 and 30-2 forthe first eye 1 and for the second eye 2.

In the single IPLS 200 embodiments, the IPLS 200 can be a stereoscopicalternating Integrated Progressive Lens Simulator 200, controlled by animage-alternator, for alternating the generating the Comprehensive PLS30-1 for the first eye 1, and subsequently 30-2 for the second eye 2,with suitable stereoscopic adjustments.

In some embodiments, similarly to FIGS. 4A-B, the Progressive LensSimulator Processor 122 can be configured to receive the generated image21 from the Image Generator 121; and to create an Off-Axis PLSsignal-component of the Comprehensive PLS signal 20 by introducing alocally varying blur 126 into the generated image 21, representative ofthe progressive lens design 123.

In some embodiments, similarly to FIGS. 4A-B, the Progressive LensSimulator Processor 122 can be configured to receive the generated image21 from the Image Generator 121; and to create an Off-Axis PLSsignal-component of the Comprehensive PLS signal 20 by introducing alocally varying curvature, or swim, 127 into the generated image 21,representative of the progressive lens design 123.

In some embodiments, similarly to FIG. 5, the Progressive Lens SimulatorPLS 100 can include a Vergence-Distance Simulator VDS 140, forsimulating a vergence for the displayed Comprehensive PLS 30 at the gazedistance. In some embodiments, the VDS 140 is integrated into the IPLS200. In some cases, the Vergence-Distance Simulator VDS 140 can beconfigured to simulate a vergence for the Comprehensive PLS 30 at thegaze distance by at least one of moving the Integrated Progressive LensSimulator 200-1 and 200-2 dominantly laterally, and shifting the createdComprehensive PLS 30 on the Integrated Progressive Lens Simulator 200-1and 200-2 dominantly laterally.

In some embodiments, similarly to FIG. 5, the Progressive Lens SimulatorPLS 100 can include a Zoom-Distance Simulator 150, for zooming theComprehensive PLS 30 to represent a change in the gaze distance. In someembodiments, the Integrated Progressive Lens Simulator PLS 200 caninclude at least one of the Vergence-Distance Simulator 140 and theZoom-Distance Simulator 150.

The Integrated Progressive Lens Simulator 200 can be configured tosimulate a primary function of the ADS 130: the optical power of theprogressive lens design 123 in the eye axis direction by creating theComprehensive PLS 30 with light rays having a vergence related to thegaze distance. As described before, the simulated optical power can beselected by combining the simulation of the distance of the viewedobject with the simulation of the axial power of the progressive lensdesign 123.

Various embodiments of the IPLS 200 will be described next, in relationto FIGS. 10-13. A shared aspect of these embodiments is that theysimulate a further aspect of the Comprehensive PLS 30: the (di)vergenceof the light rays, emanating from the viewed object according to thegaze distance. The (di)vergence is often thought of as an importantcomponent of the overall visual experience, used by our brain to analyzeand perceive the viewed images. An aspect of the embodiments in FIGS.1-8 was that the Comprehensive PLS 30 of the PLS 100 was generated byflat OPS Displays 124, thus generating flat wavefronts. These flatwavefronts do not fully represent the true viewing, or gaze, distance.In contrast, FIGS. 10-13 illustrate embodiments of the IPLS 200 that atleast have the capacity to generate the Comprehensive PLS 30 with anon-flat wavefront, the light rays diverging from every image point,thereby representing the viewing, or gaze distance more faithfully, morelife-like.

FIG. 10 illustrates that the IPLS 200 can include a Micro ElectroMechanical System (MEMS) Laser Scanner 201 that includes a light, orlaser source 208, for generating and projecting a light; and a XYscanning mirror 206, for reflecting and scanning the projected light asan XY-scanned light 209. In this IPLS 200 the light source 208 can be anLED, a collection of different color LEDs, a laser, a collection ofdifferent color lasers, and a digital light projector. The MEMS LaserScanner 201 can include a base 202, such as a frame, and afirst/Y-scanner hinge system 203 h, to rotate a Y-rotating frame 203.The Y-rotating frame 203 can support a drive coil 204 that is energizedthrough a control line 207. When a current is flowing from the controlline 207 to the Y-rotating frame 203, it induces a magnetic field in thedrive coil 204. A magnet 205, positioned under the Y-rotating frame 203exerts a torque on the energized drive coil 204, thereby rotating theY-rotating frame 203.

The IPLS 200 can further include a second/X-scanner hinge system 206 h,optionally embedded in the first/Y-scanner hinge system 203, forreflecting and scanning the projected light by the XY-scanning mirror206 in two spatial dimensions as the XY-scanned light 209. ThisX-scanner hinge system 206 h can be driven by various embodiments ofelectro-mechanical actuators. The scanning speed of the MEMS LaserScanner 201 can be high enough so that it projects the Comprehensive PLS30 with a high enough refresh rate that the patient perceives it asrealistic. Helpfully, the images used in evaluating vision, aretypically static, or only slowly moving, thus the demands on the refreshrates and therefore scanning rates are lower than, e.g. in fast-actionvideo games or live TV.

FIG. 11A illustrates another embodiment of the IPLS 200. This IPLS 200includes a Micro Electro Mechanical System (MEMS) Deformable Mirror 210that includes a light/laser source 215, for generating and projecting alight; and a deformable mirror 214, for reflecting and scanning theprojected light into an XY-scanned light 216. The light/laser source 215can be an LED, a collection of different color LEDs, a laser, acollection of different color lasers, or a digital light projector. Thedeformable mirror 214 can include a base 211, actuator electrodes 212,and an array of actuators 213, for deforming the deformable mirror 214in a segmented manner. In the shown IPLS 200, the actuators 213 aredeformable as well. Each segment of the deformable mirror 214 canredirect the XY-scanned light/laser 216, as the light is scanned acrossthe deformable mirror 214.

FIG. 11B illustrates another embodiment of the IPLS 200 that includes aMicro Electro Mechanical System (MEMS) Actuated Mirror Array 220, with alight/laser/digital light source 225, for generating and projecting alight. The light source 225 can include at least one of a LED, an LEDgroup, a laser, a laser group, a scanning light source, and a digitallight projector. The MEMS Actuated Mirror Array 220 can include a base221, supporting an actuator array 222, actuator electrodes 223, to carrycontrol signals for the actuators 222, and an array of actuatablemirrors 224, for actuably reflecting the light from the laser/lightsource 225 into a XY-scanned light/laser 226.

In the embodiments of FIGS. 11A and 11B the light can be provided in ascanned manner, or in a simultaneous manner, illuminating all mirrorsegments in FIG. 11A, or all actuatable mirrors in FIG. 11B essentiallysimultaneously. The latter embodiments can use a digital light projector225, for example.

FIG. 12A shows an IPLS 200 that includes a Microlens Array Light FieldSystem 230 that includes an Off-Axis Progressive Lens Simulator 231, forthe generating the Off-Axis PLS 50. This Off-Axis Progressive LensSimulator 231 can be the Off-Axis PLS 120 from FIGS. 1-7. The MicrolensArray Light Field System 230 can also include a microlens array 232, forreceiving the generated Off-Axis PLS 50, and for transmitting it as adivergently propagating light field 233, to simulate a progressive lenspower in the eye axis direction, a vergence related to the gazedistance, or a combination of these two, as described earlier. Microlensarrays are favored in related opto-electronic systems, such as virtualreality goggles, to create very life-like visual experiences bygenerating light fields with non-flat wavefronts.

FIG. 12B illustrates another embodiment of the IPLS 200. This IPLS 200is analogous to the IPLS 200 in FIG. 11B with one difference: itutilizes curved mirrors in place of the flat mirrors of the IPLS 200 ofFIG. 11B. As such, this IPLS 200 includes a Micro Electro MechanicalSystem (MEMS) Curved Mirror Array 240, including a digital lightprojector, or light source 245, for generating and projecting a light,the light source 245 including at least one of a LED, an LED group, alaser, a laser group, a scanning light source, and a digital lightprojector. The IPLS 200 further includes a base 241, an actuator array242, controlled by actuator electrodes, and an array of actuatablecurved mirrors 244, for reflecting the light to generate a vergencerelated to the gaze distance; wherein the curved mirrors 244 include atleast one of fixed mirrors and actuatable mirrors. The light, reflectedfrom the curved mirrors forms a divergently propagating light field 246.In some embodiments, the curvature of the actuatable curved mirrors 244can be modified according to the gaze distance, to further increase thelife-like divergence of the wavefront.

FIG. 12C illustrates yet another embodiment of the IPLS 200 thatincludes a LED Projector Array 250 that includes a base 251, a LED array252, controlled by control electrodes 253, for creating theComprehensive PLS 30 with a divergently propagating curved wavefront254, for the simulating the progressive lens power in the eye axisdirection, in combination with generating the Off-Axis progressive lenssimulation.

FIG. 12D illustrates yet another embodiment of the IPLS 200 thatincludes an actuated deformable display 259. As with other embodiments,this one can also be formed on a base, or substrate 256. A set ofcontrol electrodes 257 may carry control signals to control actuators ofan actuator array 258. The deformable display 259 can be deformablydisposed on top of the actuator array 258. The deformable display 259can be an OLED display, and any soft, flexible, or deformableequivalents. The actuators 258 can deform the display 259 by expandingand contracting in a vertical, normal direction. An aspect of thisDeformable display 255 embodiment is that it is capable of emitting anon-flat wavefront, thus improving the life-like divergence of theemitted wavefront.

FIG. 13 illustrates a method 101 i of operating the Progressive LensSimulator 100. Here the label “i” refers to the PLS 100 beingintegrated, such as the IPLS 200. The method 101 i can include thefollowing steps.

(a) tracking 102 i an eye axis direction by Eye Tracker ET 110 todetermine a gaze distance;

(b) creating 103 i a Comprehensive Progressive Lens Simulation (PLS) bythe Integrated Progressive Lens Simulator IPLS 200, by simulating aneffect of an Off-Axis Progressive Lens Simulator OPS 120, and an effectof an Axial Power-Distance Simulator ADS 130;

(c) shifting 104 i the Comprehensive PLS by a Vergence-DistanceSimulator VDS 140 to vergence appropriate for the gaze distance; and

(d) zooming 105 i the comprehensive PLS by a Zoom-Distance Simulator ZDS150 to simulate transitions of the gaze distance.

4. Head-Mounted Progressive Lens Simulator

FIGS. 14-15 illustrate a head-mounted PLS 260, secured to a patient'shead by a head-mount 262. The head-mounted PLS 260 can include anIntegrated Progressive Lens Simulator (IPLS) 200 and XYZ position ormotion sensors 263. The IPLS 200 can be any of the IPLS 200 embodimentsdescribed in relation to FIGS. 9-13. Some embodiments of thehead-mounted PLS 260 can include any embodiment of the PLS 100.

In some detail, embodiments of the PLS 100 can include an Eye Tracker110, for tracking an eye axis direction to determine a gaze distance;and the Integrated Progressive Lens Simulator 200, for creating aComprehensive Progressive Lens Simulation (PLS) 30 by simulating aprogressive lens power in the eye axis direction, in combination withgenerating an Off-Axis progressive lens simulation (Off-Axis PLS) 50. Inthese embodiments, the Eye Tracker 110 and the Integrated ProgressiveLens Simulator 200 can be implemented in a head-mounted display, virtualreality viewer, or goggles. Next, two specific embodiments of thehead-mounted PLS 260 are described in more detail.

FIG. 15A illustrates a double LCD head-mounted PLS 270, as an embodimentof the head-mounted PLS 260. The double LCD head-mounted PLS 270 caninclude a backlight 271; a first liquid crystal display 272, positionedin front of the backlight 271; a second liquid crystal display 273,spaced apart from the first liquid crystal display 271 by a spacer 274,for together simulating a progressive lens design 123 by creating alight field effect, and thereby a Comprehensive PLS 30. The double LCDhead-mounted PLS 270 can further include binocular viewing lenses 275,and eye tracker 277 that can be an embodiment of the Eye Tracker 110.Because of the extreme spatial constraints, the eye tracker 277 can beconfigured to track the eye movements from high angles. In otherembodiments, an IR reflective visible transmissive mirror can be used aspart of the eye tracker 277, in analogy with the similar mirrors 146 inthe embodiment of FIG. 7. Finally, the above elements can be energizedand controlled by a driver electronics 276. The elements 271-276together can be thought of as forming the IPLS 200.

The field of virtual reality goggles is rapidly expanding. These gogglesare more and more capable of generating life-like visual experiences,and therefore their technology can be promisingly implemented andadapted for use in embodiments of the head-mounted PLS 260 to create theComprehensive PLS 30.

In particular, the light field effect generated by the head-mounted PLS260 can be three dimensional, or four dimensional. The latter (4D)technology also represents the depth of focus perception, making objectsthat are in front or behind the object plane blurrier. Doing so furtherenhances the life-likeness of the visual perception.

Some embodiments of the PLS 100 can use not only XYZ position sensorsbut XYZ motion sensors 263. Picking up not only the position anddirection of the head-mounted PLS 260/270, but also sensing a motion ofa wearer of the head-mounted PLS 260/270 can be integrated into thecontrol software that runs on the driver electronics 276, or in aseparate computer. The XYZ motion sensor 263, or motion sensors caninclude at least one of an accelerometer, a gyroscope, and amagnetometer. These can all contribute to sensing the motion, positionand direction of the user's gaze.

FIG. 15B illustrates another, related embodiment of the head-mounted PLS260: a microlens array head-mounted PLS 280. The microlens arrayhead-mounted PLS 280 can include a backlight 281; a liquid crystaldisplay 282, positioned in front of the backlight 281; and a microlensarray 283, spaced apart from the liquid crystal display 282 by a spacer284, for together simulating a progressive lens design 123 by creating alight field effect, and thereby creating a Comprehensive PLS 30. Asdiscussed earlier in the context of FIG. 12A, microlens arrays cancreate light field effects and non-flat wavefronts particularlyeffectively, thereby increasing the life-likeness of the visualexperience of the Comprehensive PLS 30. The light field effect can bethree dimensional or four dimensional.

The microlens array head-mounted PLS 280 can further include binocularviewing lenses 285; and an eye tracker 287. As before, the eye tracker287 can be an embodiment of the Eye Tracker 110. In some cases, the eyetracker 287 has to be able to work at high angles, or with the help ofan IR reflective, visible transmissive mirror, similar to the mirror 146in FIG. 7. Finally, the above elements can be energized and controlledby a driver electronics 286. The elements 281-286 together can bethought of as forming the IPLS 200.

This embodiment can further include at least one XYZ position,direction, or motion sensor 263, for sensing a position, direction, ormotion of a wearer of the head-mounted PLS 280. This sensor can sensethe position, the direction, or the motion of the head-mounted PLS 280,thereby aiding the generation of the Comprehensive PLS 30.

All embodiments of FIGS. 14 and 15A-B can further include a housing278/288 for accommodating the Eye Tracker 277/287, and the IntegratedProgressive Lens Simulator 200. Also, some of the computers used in thePLS 100 and IPLS 200, such as the Progressive Lens Simulator Processor122, can be implemented in a self-standing computer, separate from thehead-mount. The self-standing computer can communicate with thehead-mounted PLS 260/270/280 via a wired connection, or via a Bluetoothconnection.

Finally, in some embodiments, head-mounted PLS 260/270/280 can involveaugmented reality glasses, wherein the Comprehensive Progressive LensSimulation 30 is generated from an image viewed via the augmentedreality glasses.

5-6. Guided Lens Design Exploration System and Method for a ProgressiveLens Simulator

As mentioned in the introductory part, FIGS. 3-15 describe ProgressiveLens Simulators 100 that generate Comprehensive Progressive LensSimulations 30, in order to enable the patients to explore manyprogressive lens designs 123 via high quality, life-like visualexperiences. A class of these PLS 100 systems can be operated by theoptometrists in the traditional manner, using only verbal feedbacks fromthe patients. In this section, additional classes of systems aredescribed, that empower the patient to control and to manage theexploration of as many progressive lens designs as they desire undertheir own control.

FIGS. 16-29 illustrate that important additional systems can be employedin some embodiments of GPS 10 to control, to manage and to acceleratethe exploration of a substantial number of progressive lens designs.These GPS 10 systems can be managed and controlled by the patient, by anoptometrist, or a vision technician. In these embodiments, the patientcan provide feedback and optionally control signals in response to theComprehensive Progressive Lens Simulation 30 of a progressive lensdesign 123. As described in the early sections, this feature is apowerful departure from existing optometry systems, at least for thelisted dozen reasons.

FIG. 16 illustrates that in embodiments, a PLS 100 can be combined witha Lens Design Exploration System for Progressive Lens Simulator (LDES)300. This combined system includes the Progressive Lens Simulator 100,for generating a Comprehensive Progressive Lens Simulation 30 utilizinga progressive lens design 123 with Design Factors 420 for a patient, andfor receiving a Visual Feedback 430 in response to the ComprehensiveProgressive Lens Simulation (PLS) 30. Several embodiments of the PLS 100have been described in relation to FIGS. 3-15. Any one of thoseembodiments, and any of their combinations can be used in thedescription below.

The LDES 300 can further include a Progressive Lens Design processor320, coupled to the Progressive Lens Simulator 100, for modifying theprogressive lens design 123 in response to the Visual Feedback 430, andtransmitting the modified progressive lens design 123 to the ProgressiveLens Simulator 100 to generate a modified Comprehensive Progressive LensSimulation 30 for the patient with the modified progressive lens design123. The progressive Lens Design Processor 320 can be part of, or evenintegrated into, the OPS 120.

The LDES 300 can further include a Feedback-Control Interface 310,coupled to the Progressive Lens Design processor 320, for receiving theVisual Feedback 430 from an operator, selected from the group consistingof a joystick, a touchpad, a mouse, an audio interface, an externaltablet GUI, and an internal visual-interface GUI, and forwarding thereceived Visual Feedback in the form of a feedback-control signal 311 tothe Progressive Lens Design processor 320. Other embodiments may includethe Eye tracker 110, coupled to the Progressive Lens Design processor320, for receiving a Visual Feedback 430 in a form of an objectivepatient vision measurement. In other embodiments, other systems canprovide objective feedbacks, including Purkinje-spot based imagers,Scheimpflug systems, and OCT systems.

Also, the Progressive Lens Design Processor 320 can base some of itscalculations of eye modeling. This may involve imaging some of theophthalmic layers in the eye, and then building an eye model like thewidely used Holladay model.

Several modes of operation of the embodiments of the LDES 300 have beencontemplated regarding the feedback. (1) Some LDES 300 are operated bythe patient himself/herself. The PLS 100 generates the Comprehensive PLS30 for the patient, who evaluates the visual experience and directlyenters a subjective feedback into the FCI 310.

(2) In other embodiments of LDES 300, the Visual Feedback may only beindirect. The patient may only express a verbal feedback, such as thelast modification made the visual experience of the Comprehensive PLS 30better or worse, and a trained operator, technician, or the optometristherself/himself may enter a control signal 311 via the FCI 310.

(3) Other LDES 300 can be based on objective patient feedback and do notrequire an active, or subjective, patient feedback. For example, the EyeTracker 110 can monitor the patient's eye movements and draw conclusionsfrom the monitoring. E.g. if the jitter of the patient's eye increases,or the patient struggles to focus in response to a modification of theComprehensive PLS 30, then the Eye Tracker 110 may report this to theProgressive Lens Design Processor 320 of the LDES 300. In response, asoftware of the Progressive Lens Design Processor 320 may conclude thatthe modification was undesirable, and undo the modification, or try adifferent one.

(4) Finally, in some embodiments of LDES 300, the objective patientfeedback, or objective patient vision measurement, such as the rapidityof the patient's eye movement, or inability to focus, may be monitorednot by a computer software but by the operator of the LDES 300, such asthe optometrist herself/himself without an express, or subjectivecooperation of the patient. In such embodiments, the monitoring operatorcan enter the feedback or control into the FCI 310 that transforms itinto a feedback-control signal 311.

The combination of the PLS 100 and LDES 300, described next, can beoperated in any of the above four modes, or in some combination of thesemodes. Sometimes the patient, technician, optometrist, or somecombination of more than one of these possible sources providing afeedback or a control input together will be referred to as “theoperator”. Also for this reason, the inputs into the FCI 310, and thesignal 311 outputted by FCI 310 can be feedback, control, and anycombination of feedback and control input and feedback and controlsignals. These possibilities and combinations will be inclusivelyreferred to as feedback-control input and feedback-control signal 311.

FIGS. 17A-F illustrate that the Feedback-Control Interface FCI 310,where an operator can enter a feedback or control input, can havenumerous embodiments. These include a (twin) joystick FCI 310-1 in FIG.17A, a touchpad-mouse FCI 310-2 in FIG. 17B, an audio interface FCI310-3 in FIG. 17C, an external tablet GUI (Graphical User Interface) FCI310-4 in FIG. 171), and an internal visual-interface GUI FCI 310-5,possibly overlaid with the visual experience of the Comprehensive PLS 30in FIG. 17E. As mentioned in the second embodiment of the LDES 300, insome cases the patient can only provide a subjective feedback which thencan be used by an operator, or technician to actually enter an inputinto an indirect FCI 310-6. FIG. 17F symbolically illustrates suchcombined operator modes. In analogous embodiments, the optometrist canenter an input into a tablet, an iPad, a fixed terminal or a GUI of theLDES 300.

It is a non-obvious, challenging task to “translate” the feedback intoan actionable command on how to modify the progressive lens design 123in response to the feedback. Several embodiments will be described nextto carry out this translation, in response to the feedback, or “VisualFeedback”. The method of exploring and changing the progressive lensdesign in response to a Visual Feedback in general will be referred toas a method 400 of Progressive Lens Simulation. Several embodiments ofthis method will be described next.

FIG. 18A illustrates the method 400 of Progressive Lens Simulation,comprising:

(a) activating 401 a progressive lens design 123 with Design Factors 420by a Progressive Lens Design Processor 320;

(b) generating 402 an image 21 by an Image Generator 121 of aProgressive Lens Simulator 100;

(c) generating 403 a Comprehensive Progressive Lens Simulation (PLS) 30,simulated from the generated image 21 by the Progressive Lens Simulator100, utilizing the progressive lens design 123;

(d) acquiring 404 a Visual Feedback 430, responsive to the generating ofthe Comprehensive PLS 30 with the progressive lens design 123;

(e) modifying 405 the progressive lens design 123 by the ProgressiveLens Design Processor 320 in relation to the Visual Feedback 430; and

(f) re-generating 406 the Comprehensive PLS 30 with the modifiedprogressive lens design 123 by the Progressive Lens Simulator 100.

The method 400 can typically involve repeating steps (d)-(e)-(f) untilthe Visual Feedback 430 indicates a satisfactory outcome of the method.

The activating 401 the progressive lens design 123 with a ProgressiveLens Design Processor 320 can be in response to a progressive lensdesign selection based on a preparatory measurement. The generating theimage 21 by an Image Generator 121 can be in response to an imageselection. Embodiments of the activating 401 are broadly understood. Theactivating 401 of the progressive lens design 123 can include recallingthe progressive lens design 123 from a memory, or from a storage medium.The activating 401 can also include that the Progressive Lens DesignProcessor 320 computes or models the progressive lens design 123 fromsome model parameters. Whichever way the progressive lens design 123 isactivated, the Progressive Lens Simulator 100 can be generating 403 aComprehensive Progressive Lens Simulation PLS 30 from the generatedimage 21 by utilizing the activated progressive lens design 123.

The above steps of the method 400 are typically performed by the PLS 100and the LDES 300. FIG. 18B illustrates steps of a method 410 of anoperator interacting with the LDES 300 to carry out the method 400. Themethod 410 can include the following steps.

(a) optionally selecting 411 a progressive lens design 123 with aProgressive Lens Design Processor 320, based on a preparatorymeasurement;

(b) optionally selecting 412 an image 21 with an Image Generator 121;

(c) evaluating 413 a visual experience of a generated Comprehensive PLS30, simulated by a Progressive Lens Simulator PLS 100 from a selectedimage 21 with a progressive lens design 123;

(d) the evaluating optionally including inspecting 414 an image regionof the generating a Comprehensive PLS 30 in an inspection direction; and

(e) providing 415 a Visual Feedback 430 to a Progressive Lens DesignProcessor 320 based on the evaluating via a Feedback-Control Interface310.

FIGS. 19A-B illustrate the method steps that were described in FIGS.11A-B. FIG. 19A illustrates the steps of the method 400 in some detailthat were described in relation to FIG. 18A. For the activating step401, the progressive lens design 123 is illustrated with a progressivelens design 123 defined by contour lines. For the generating an imagestep 402, a generated image 21 is shown. For the generating thecomprehensive PLS step 403, it is shown that generating theComprehensive PLS 30 from the generated image 21 can involve introducinga blur 126 into the peripheral regions of the image 21, and introducinga swim 127 by bending the straight lines of the image 21, both based ondetailed optical calculations. The acquiring step 404 can involve ameasurement of a jitter of an angle α of the visual axis of the eye asshown: excessive jitter may indicate excessive discomfort with theComprehensive PLS 30 of the progressive lens design 123.

FIG. 19B illustrates the steps of the method 410, executed by anoperator of the PLS 100-LDES 300 system. The steps of the method 410 areshown in relation to the steps of the method 400 that are executed bythe combined PLS 100-LDES 300 system itself. These steps once againclosely correspond to the steps described in FIG. 18B.

FIGS. 20A-B illustrate Designs Factors 420. A large number of designfactors can be used, and different lens manufacturers often have theirown specialized set of Design Factors. By way of example, DesignsFactors 420 may include contour lines 421, pupil height or opticalcenter 422, corridor, or channel width 423, corridor length 424,near-vision nasal offset 425, progression pitch 426, prism, or prismangle 427, cylinder orientation 428, progressive prism, Zernikecoefficients, and many-many others. With the ever-strengtheninginfluence of the Schneider free-form lens manufacturing technology, anyencoding of the lens topographic, contour or height map can be used asdesign factors.

Some design factors can be supplemented by preparatory measurements bythe optometrist. One example is that the optometrist can observe theglass-positioning height, where the patient is wearing the glasses onher/his nose. The optometrist can shift the height of the optical center422 Design Factor to account for the patient's individualglass-positioning height.

FIG. 20B illustrates that the collection of these individual designfactors DF₁, DF₂, . . . , DF_(k) together can be thought of as defininga Design Factor Space. In this Design Factor Space, the specificprogressive lens design can be represented with a Design Factor vectorDF. The exploration of the progressive lens designs 123 can then bethought of as a guided wandering of the Design Factor vector DF througha series of iterations DF(1), DF(2), . . . , until the optimal DesignFactor vector DF(final) is reached. (For clarity, the subscripts 1, 2, .. . k, refer to the individual Design Factors as components of the DFvector, whereas the indices 1, 2, . . . n in brackets refer to thenumber of iterative steps during the course of the iterative explorationof the progressive lens designs 123.)

FIGS. 20C-D illustrate the analogous organization of the Visual Feedback430. These Visual Feedbacks 430 can be of different types or classes,including the followings.

(a) A subjective patient feedback via a Feedback-Control Interface;

(b) an objective patient vision measurement;

(c) an eye tracker image/data from an Eye Tracker;

(d) a direct patient feedback;

(e) an indirect patient feedback;

(f) an operator control input;

(g) an operator command;

(h) an operator response to a proposition; and

(i) an operator selection.

The term “Visual Feedback” is used broadly in this document. It caninclude subjective feedbacks, like the patient expressing a subjectiveevaluation of the visual experience. It can be an objective feedback,like a measurement of a jitteriness of the patient's visual axis. It canbe a direct feedback directly entered by the patient into the FCI 310.It can be an indirect feedback, the patient verbally stating anexperience or preference and an operator entering the correspondingfeedback into the FCI 310. The feedback can come from a single operator,such as the patient, or from more than one operator, such as the patiententering a partial visual feedback and the operator entering anothercomplementary feedback. Also, the Visual Feedback 430 can be simply afeedback, or a control, or command input, where the operator translatedthe visual experience into a control, command, selection, or response.

In each of the above classes, there can be a large number of specificVisual Feedbacks 430. To expand by examples, the subjective VisualFeedbacks can include the patient subjectively perceiving the lowernasal quadrant blurry, the lower temporal quadrant blurry, the nasalprogression region has too much swim, progression corridor too long,progression corridor too wide. The subjective Visual Feedback 430 caninclude requests, commands, or other control input, such as the patientrequesting rotate cylinder orientation angle, increase prism, anddecrease prism progression. The objective Visual Feedback 430 caninclude the optometrist observing that the patient's visual inspectiondirection, or visual axis, being too jittery, or the patient havingdifficulty focusing on the presented object, and inputting acorresponding feedback-control input into the FCI 310. AI of thesepossibilities are included in the scope of “Visual Feedback 430”.

FIG. 20D illustrates that the individual Visual Feedbacks VF₁, VF₂, . .. , VF₁ can be thought of as forming a Visual Feedback vector VF 430, inanalogy to the Design Factor vector DF. In various embodiments, thelength of the DF vector may not be equal to the length of the VF vector.In this approach, in each iteration of the progressive lens explorationby the method 400, a new Visual Feedback vector VF(n) is received froman operator, patient, or measurement system, such as the Eye tracker110, and in response, the combined PLS 100-LDES 300 system modifies theDesign Factor vector DF.

FIG. 16 shows that in several embodiments of the Lens Design ExplorationSystem 300 for a Progressive Lens Simulator 100, the Progressive LensDesign Processor 320 includes a Visual Feedback-to-Lens Design TransferEngine FLE 325, that plays an important role in modifying theprogressive lens design 123 in response to the Visual Feedback 430.

An important role of this Visual Feedback-to-Lens Design Transfer EngineFLE 325 is to “translate” the received Visual Feedback 430 into amodification of the Design Factors 420. In an example, if the patientinputs the Visual Feedback 430 that “lower nasal region too blurry” intothe Feedback Control Interface 310, it is a non-obvious task totranslate this specific Visual Feedback 430 into which Design Factors tochange to what degree to reduce the blurriness in the lower nasalregion. A large amount of optical modelling, ray-tracing,patient-testing, and refinement are needed to establish that which setof Design Factors 420 need to be modified to what degree to respond tothe various Visual Feedbacks 430. This complex knowledge is embodied,managed and carried out in the Visual Feedback-to-Lens Design TransferEngine FLE 325.

FIG. 21 illustrates the usefulness of the above-introduced vectorconcepts to visualize the exploration of progressive lens designs. TheDesign Factors DF₁, DF₂, . . . , DF_(n) define a multi-dimensionalDesign Factor space. A specific progressive lens design 30 isrepresented by a specific Design Factor vector DF=(DF₁, DF₂, . . . ,DF_(k)) in this space. The PLS 100 generates an initial ComprehensivePLS 30 for the patient, using a specific progressive lens design 123,now represented by a Design Factor vector DF(1). The PLS 100 thenacquires a Visual Feedback vector VF(1)=(VF₁, VF₂, . . . , VF₁),responsive to the Comprehensive simulation of the Progressive LensDF(1). The LDES 300 system modifies the Design Factor vector DF(1) intoDF(2) in response to the Visual Feedback vector VF(1). FIG. 21illustrates this process via the n-th iteration. The LDES 300 modifiesthe Design Factor vector DF(n) into DF(n+1) in response to the VisualFeedback vector VF(n) by adding:ΔDF(n)=DF(n+1)−DF(n)  (1)

Visibly, the ΔDF(n) increment vectors form a search path in the DesignFactor space. As the combined PLS 100 and LDES 300 system performs themethod 400, in interaction with an operator, patient, or optometristaccording to the method 410, this search path converges to a customizedoptimal Design Factor vector DF 420 that best suits the patient's needs.

FIGS. 22A-B illustrate the case, when the relationship is approximatelylocally linear between the Visual Feedback vector VF 430 and the DesignFactor vector DF 420. In such cases, the Visual Feedback-to-Lens DesignTransfer Engine 325 can utilize a Visual Feedback-to-Design Factormatrix VFDF 326 for the modifying the progressive lens design by aMatrix Method. (Matrices and vectors are referenced with boldface.)

FIG. 22A illustrates a 4×4 representation of the VFDF matrix 326. Theequation below is the extended form of the above Eq. (1). As discussedearlier, in general the length “k” of the DF vector 420 is not equal tothe length “l” of the VF vector 430, so often the VFDF matrix 326 is anon-square, rectangular matrix.

The elements of the VFDF matrix 326 represent how to translate theelements of the Visual Feedback vector 430 into a change of the DesignFactor vector 420. Typically, more than one element of the DF vector 420need to be changed in a correlated manner. These correlations thattranslate the inputted Visual Feedback vector VF 430 into a designfactor vector DF 420 constitute the elements of the VFDF matrix 326.

FIG. 22B illustrates this translation by an example. The VFDF matrix 326is a 7×9 matrix in this embodiment. Visibly, the Visual Feedback vectorVF 430 includes subjective patient feedbacks, like the patientindicating that the optical center too high, as well as objectivefeedbacks, like the Eye tracker 110 measuring that the visual inspectiondirection, or visual axis of the patient is too jittery. As an example,if the patient's Visual Feedback is “lower temporal quadrant blurry”,that can be represented with a binary Visual Feedback vector 430 ofVF=(0,1,0,0,0,0,0), or with a quantitative expression x, representinghow blurry the image is VF=(0,x,0,0,0,0,0), e.g. determined by anobserving optometrist. If the non-zero elements in the second column ofthe VFDF matrix 326 are VFDF₃₂ and VFDF₇₂, that means that the bestresponse to the above Visual Feedback is to modify the Design Factorvector DF 420 with ΔDF=VFDF*VF=(0,0,VFDF₃₂,0,0,0,VFDF₇₂,0,0), i.e. toincrease or decrease the progression corridor width by VFDF₃₂, dependingon the sign of VFDF₃₂; and to rotate the cylinder clockwise orcounter-clockwise VFDF₇₂, depending on the sign of VFDF₃₂. A widevariation of related and analogous embodiments exists, including thesize of the VFDF matrix 326, and the choice of factors both in the DFvector 420 and the VF vector 430.

FIG. 23A illustrates that in many instances, the exploration of theprogressive lens designs 123 may require non-local and non-linear steps,which therefore are not well represented by a VFDF matrix 326. Forexample, the patient's exploration may end up in a region of the DesignFactor space, where the patient keeps giving the Visual Feedback 430that no change is improving the visual experience. In simple terms, thatthe search got stuck in a dead-end, and likely the optimal solution isin a far away, distinct region of the Design Factor space that cannot beeasily reached. In such cases, the patient's search may be best assistedby a large jump to another region in the Design Factor space, or by someother robust move. In general, such moves will be referred to asperforming Search Management steps 450. To facilitate such non-localand/or non-linear moves, the Lens Design Exploration System 300 caninclude a Search Guidance Engine SGE 330, coupled to the ProgressiveLens Design Processor 320, for performing a Search Management step 450,including at least one of

(a) reversing a search path in a Design Factor space;

(b) reverting to a preceding bifurcation in a search path in a DesignFactor space;

(c) jumping to another Design Factor vector;

(d) changing a number of the Design Factors;

(e) fixing a design factor;

(f) changing a speed of performing the method; and

(g) evaluating whether search has been successful.

FIG. 23B illustrates that these Search Management steps 450 can becarried out in an interactive manner in some embodiments. In suchembodiments, the Search Guidance Engine 330 may be coupled to theFeedback-Controller interface 310, for performing the Search Managementstep interactively 455 by

(a) proposing to an operator to select a Search Management step 450;

(b) receiving a selection of a Search Management step 450 from theoperator; and

(c) initiating an execution of the selected Search Management step 450.The initiating 455(c) may involve the Search Guidance Engine 330instructing the Progressive Lens Design Processor 320 to carry out theselected Search Management step 450.

In these embodiments, the Search Guidance Engine SGE 330 may not simplycarry out a Search Management step 450 on its own directive, such asfixing a Design Factor. Instead, it may interactively propose acontemplated Search Management step 450 to the patient and act only uponreceipt of a response. By way of an example, the SGE 300 may prompt thepatient via the FCI 310: “Should we fix the location of the opticalcenter, and restrict the search to the remaining Design Factors?” in thestep 455(a), then receive the selection from an operator, e.g. “Yes” instep 455(b), and initiate carrying the selection, such as reduce thenumber of Design Factors by one, thereby reducing the dimensions of theDesign Factor space by one in step 455(c). In some embodiments, theSearch Guidance Engine 330 may offer alternatives: “should we go back toa preceding bifurcation in a search path in a Design Factor space, orshould we reverse the search path?”, and carry out the responsive wishof the patient or the operator.

In some embodiments, the Search Management method 455 can involve

(d) the patient storing a selected first lens design 123;

(e) continuing the Lens Design Exploration by any embodiment of themethod 400; for example, by reversing the search path by step 450(a), orreverting to a preceding bifurcation by step 450(b);

(f) selecting a subsequent second lens design 123; and

(g) comparing the stored first lens design with the second lens design.

In some embodiments, the Search Guidance Engine SGE 330 may beintegrated with the Progressive Lens Design Processor 320.

An aspect of the described embodiments that rely on subjective VisualFeedbacks 430 is that the exploration is not guided by objective meritsand measures. In practice, this can, and does, reduce the repeatabilityof the exploration. When the same patient repeats the same lens designexploration at a later time, it has been observed that she/he sometimesarrives at different lens designs: a potentially unsatisfying outcome.

Repeatability and the soundness of the lens selection can be enhanced byPLS 100 systems acquiring and prioritizing objective Visual Feedbacks430. One such objective Visual Feedback 430 has been already describedby the Eye tracker 110 measuring a jitter of the visual axis. Anotherclass of objective Visual Feedbacks 430 can be generated by integratingdiagnostic systems into the PLS 100. Such systems may includePurkinje-spot-based imaging systems, OCT systems, Scheimpflug imagingsystems, wavefront analyzers, corneal topographers, slit lamps, andrelated other systems.

Another class of improvements can be introduced by using eye models toevaluate, organize and analyze the diagnostic measurements. Several eyemodels are known in ophthalmology, often used in preparation forcataract surgery, such as the Holladay and Hoffer models.

The next class of embodiments provides an improvement in this direction.These embodiments define a quantitative metric that can prove quiteuseful to make the lens design exploration more effective, and lead torepeatable outcomes.

FIGS. 24A-B introduce the concept of objective, quantitative measuresand metrics to assist the exploration of the progressive lens designs123. Such objective measures are already used by the designers ofprogressive lenses. However, presently these measures are used bycomputer programs separately from the patient's exploration, much afterthe patient visits the optometrist's office. Typically, the computercode performs the lens design search separated from the patient byhundreds, or thousands, of miles and hundreds, or thousands, of hours.In typical cases today, an optometrist determines a couple visioncorrection parameters in her office, for example, the optical powers forthe near and distance vision regions, and then sends these parameters toa progressive lens designer. Weeks later, the progressive lens designerruns a computer code that computes a contour map that optimizes the lensperformance with the sent optical powers. This optimization uses somequantitative measures to characterize the lens' performance, typicallydeveloped by the lens design company.

However, in today's practice, this optimization is not interactive; thepatient is simply given the end product. There is no visual feedbackfrom the patient during the design process, and no chance for thepatient to indicate that the computer-designed progressive lens is farfrom optimal for the patient's specific needs. Thus, if the patientdecides that the visual experience with the progressive lens isunsatisfactory, then he returns the lens and a time consuming,inefficient back-and-forth process starts between the lens designingcompany and the patient, typically shipping the glasses back-and-forth,and involving additional grinding of the lens, often taking many weeks,and wasting valuable time from all involved.

The here-described embodiments in general offer a substantiveimprovement over this state of the art by simulating a progressive lensdesign 123 in real time in steps 401-403, then acquiring a VisualFeedback 430 from the patient in real time in step 404, and modifyingthe progressive lens design 123 in real time in step 405, performing allthese steps iteratively. The now-described embodiments offer a furtherimprovement by introducing objective lens merit factors 460 and weavethese lens merit factors 460 into the modifying the progressive lensdesign step 405 of the method 400.

These lens merit factors 460 can include:

(a) an improved visual acuity in one of a near and a far vision region;

(b) a reduced astigmatism in one of a near and a far vision region;

(c) a reduced swim in one of a near and a far vision region;

(d) a reduced blur in one of a near and a far vision region;

(e) a suitable progressive region;

(f) an alignment of a cylinder;

(g) a suitable prism; and

(h) a suitable progressive prism.

Many-many more Lens Merit factors can be developed, including moretechnical ones, such as the integral of a measure of an astigmatism overa vision region, or the coefficient of a Zernike polynomial of the lens,possibly narrowed to a region.

FIG. 24B shows that, as before, these Lens Merit Factor 460 can bethought of as a Lens Merit vector LM 460, with components LM==(LM₁, LM₂,. . . , LM_(m)). In some cases, these individual Lens Merit factors canbe combined into a single global Lens Merit with suitable weightfactors. For example, this single combined Lens Metric can be |LM|, theoverall length, or magnitude of the LM vector, combined from the sum ofthe squares of the individual components: |LM|=[Σ_(i)LM_(i) ²]^(1/2)).To simplify the notation in the below complex discussion, the labels420, 430 and 460 are sometimes suppressed for the DF, VF and LM vectors.

FIG. 25 illustrates the impact of introducing the Lens Merit factorsinto the progressive lens designs as a quantitative metric to assist theexploration of lens designs. In embodiments, the modifying step 440 canbe expanded into the modifying the one or more Design Factors step 465,the modifying step 465 being based on the Visual Feedback 430 and on oneor more Lens Merit factors 460. In the vector notation, the nt^(h)iteration of the Design Factor vector DF(n) gets modified byΔDF(n)=DF(n+1)−DF(n), where ΔDF(n) is computed based on the VisualFeedback vector VF(n) 430 and the Lens Merit factor vector LM(n).

FIG. 26 illustrates that when VF(n) and LM(n) determine ΔDF(n)approximately linearly, then the modifying step 465 can specificallyinvolve a modifying step 470 that includes performing a Merit-MatrixMethod step by modifying the one or more Design Factors DF(n) 420 inrelation to the Visual Feedback VF(n) 430 and on one or more Lens Meritfactors 460 with a Visual Feedback+ Lens Merit-to-Design Factor matrix329. As illustrated, this method 470 is driven not only by the VisualFeedback VF 430, but also by the objective Lens Merit LM 460. These twovectors VF and LM can be combined into a composite VF+LM vector whoselength equals the length of the two vectors separately. Next, the VFDFmatrix 326 can be extended by a Lens Merit-Design Factor matrix LMDF328, the juxtaposition of the two matrices together forming the VisualFeedback+ Lens Merit-to-Design Factor matrix 329. Multiplying theextended VF+LM vector with the extended VFDF+ LMDF matrix determinesΔDF(n) in these embodiments.

FIG. 27 illustrates some of the benefits of such Lens Merit-computingembodiments. For a specific progressive lens design 123, represented bya Design Factor vector DF, the components of the Lens Merit vector LMcan be calculated. FIG. 27 shows only one component of the LM vector. Incertain cases, this can be the global Lens Merit |LM|, mentionedearlier. In other embodiments, the method 475 described below can bepracticed for several of the Lens Merit components simultaneously. Byway of example, the shown component of the LM vector, LM_(i), can be theintegrated astigmatism over the near vision region. The lower LM_(i),the integrated astigmatism, the better the progressive lens design 123.In the context of FIG. 27, this means that the optimal progressive lensdesign 123 corresponds to the DF=(DF₁, DF₂) vector 420 that points tothe extremum of the Lens Merit function. For a multicomponent Lens Meritvector LM, the individual Lens Merit factors LM_(i) can be minimized ina coupled, interconnected manner. The issue of the Lens Merit functionhaving more than one extrema (as illustrated in FIG. 27) will beaddressed below.

In some existing systems, where the progressive lens design 123 isdetermined by a lens design code without a Visual Feedback 430, the lensdesign code optimizes the lens design only by modifying the DesignFactors DF₁ and DF₂ so that the Design Factor vector DF points to thelocation of the local minimum of the Lens Merit function, shown in FIG.27. Such local minima indicate that the corresponding combination of theDesign Factors DF₁ and DF₂ optimizes the visual experience according toray tracing, optical measurements and large number of collected patientexperiences, but without a feedback of the specific patient whoseprogressive lens is being designed. Such optima reflect a compromisebetween the different design goals.

The methods 465,470 and 475 transcend previously described systems bymodifying the one or more Design Factors 420 based on the VisualFeedback 430 in combination with one or more Lens Merit factors 460.Therefore, the methods 465/470/475 transcend the method 440 thatprimarily relies on the Visual Feedbacks 430 but does not use the LensMerit 460. These methods also transcend the present progressive lensdesign codes that rely only on computing Lens Merit 460, but do not usethe Visual Feedback 430. Combining the two design-drivers VF 430 and LM460 is challenging, for several reasons. First, to be able to combinethese design-drivers requires the Visual Feedbacks 430 to be quantified,but the quantification of the various subjective Visual Feedbacks 430 isfar from obvious. Second, a lot of thoughtful decisions are needed toselect the weight factors that combine the VF and LM factors andcomponents. Further, communicating the contemplated lens design updatesto the patient in a relatable manner is also a non-trivial task, amongothers.

Typically, the patient's Visual Feedback VF 430 may request, or prompt,a modification of the Design Factor vector DF 420 that is different fromthe DF 420 pointing to the local minimum of LM. This can happen for avariety of reasons including the following. (a) The local minimum of LMin the lens design code was determined by averaging feedback from alarge number of patients, and thus may not be optimal for any specificpatient. (b) The search may involve optimizing several LM componentssimultaneously. Compromising between these coupled searches can make aDF 420 optimal for a vector other than the minimum for the coupledsearch. (c) The exploration uses a combination of the VF 430 and the LMvectors 460 to modify the DF vector 420. The subjective VF 430 inputsmay nudge the exploration towards new design goal compromises. Also, insome embodiments, the patient may be offered to shift the mixing andweighing parameters, again moving the local minimum.

In some embodiments, the modifying step 475 may include modifying theDesign Factors locally in a Design Factor space by utilizing at leastone of a gradient descent method, a conjugate descent method, and ahill-climbing method. In FIG. 27, this can be implemented at any DF(n)vector by, e.g., searching out the locally steepest downhill gradientthat promises to move the exploration towards the local minimum in thefastest manner. Since these methods build on local information about howthe LM_(i) components depend on their DF_(j) coordinates, the method 475is characterized as a local modification.

However, FIG. 27 also illustrates that such local methods may not reachthe global minimum of the Lens Merit function LM 460, if the globalminimum is at some distance away from the local minimum in the DesignFactor space. In this typical case, the local steepest descent gradientmethods often guide the search only into the local minimum, where themethods may get stuck and never reach the global minimum, located adistance apart.

FIGS. 28A-B illustrate that in such cases a modifying 480 of the one ormore Design Factors 420 based on the Visual Feedback 430 and on one ormore Lens Merit factors 460 may include modifying 480 the Design Factor420 non-locally in the Design Factor space. This non-local modification480 may utilize at least one of a simulated annealing, a geneticalgorithm, a parallel tempering, a stochastic gradient descent,stochastic jumps, and a sequential Monte Carlo.

In the shown example, the lens design exploration may be gettingtemporarily stuck around a Local Optimum 482 when only local searchmethods 475 are utilized. It is noted that the optimum can be either aminimum or a maximum. In FIG. 27, the desired design corresponded to aminimum of the LM function, in FIGS. 28A-B, it corresponds to a maximum.The local modification methods 475 can get stuck in the Local Optimum482 that is distinctly away from the Global Optimum 484. Embodiments ofthe method 480 can free the stuck search by employing the above listednon-local methods. A large number of additional non-local modificationsare also known in the arts. One such method is to implement large,stochastic jumps, to get a stuck search “unstuck”. FIG. 28A illustratesthat a search that uses only local techniques may have gotten stuckaround the Local Optimum 482, but performing a stochastic jumpembodiment of the non-local method 480 can get the search unstuck, andenable it to find the Global Optimum 484. FIG. 28B illustrates the samestochastic jump embodiment of the method 480 from a perspective view.Visibly, the stochastic jump is taking the stuck search from thejump-start to the well-separated jump-end point in the Design Factorspace, enabling it to reach the Global Optimum 484.

FIG. 29A illustrates that—in analogy to method 450 in FIG. 23A—any oneof the 465-480 embodiments of the modifying the one or more DesignFactors step based on the Visual Feedback 430 in combination with on oneor more Lens Merit factors 460 can comprise a Search Management step490, performed by the Search Guidance Engine 330, that can include atleast one of the followings.

(a) Reversing a search path in a Design Factor space;

(b) reverting to a preceding bifurcation in a search path in a DesignFactor space;

(c) jumping to another Design Factor vector;

(d) changing a number of the Design Factors;

(e) fixing a Design Factor;

(t) changing a speed of performing the method; and

(g) evaluating whether search has been successful.

As discussed in relation to the analogous method 450, such SearchManagement methods, or steps 490 can be very helpful when the patient'sexploration of lens designs becomes disoriented, or stuck. In otherembodiments, their primary value can be that they accelerate theprogressive lens design exploration, making it much more efficient. Inthis method 490 the added factor relative to the method 450 is that aquantitative metric is involved in the form of the Lens Merit 460. Theintroduction of such a quantitative metric greatly narrows down thesearch. For example, the large number of possible ways of modifying theDesign Factors 420 that need to be evaluated when practicing the methods440 and 450 can be reduced substantially by narrowing the search topaths that the Lens Merit 460 identifies as locally optimal. In theexample of FIG. 29A this is demonstrated by a non-merit guided searches440-455 needing to evaluate all possible Design Factor modifications,whereas the Lens Merit factor guided searches 465-490 needing toevaluate only moves along the narrow ridge, preferred by the Lens Merit460, or in some vicinity of this ridge.

As before, FIG. 29A shows the search path on the ridge for only one LMcomponent for clarity. As the Search Guidance Engine 330 is monitoringthe search and senses that the ridge is getting lower (as shown), i.e.the search by the local modifying method 475 is getting farther frompossibly finding a local maximum of the Lens Merit 460, the SearchGuidance Engine 330 may activate the method 490(a) and perform a SearchManagement step to reverse the search path. Here the availability of thequantitative metric of the Lens Merit 460 is quite useful, as thereverse search path in the Design Factor space is a well-defined ridgeof the Lens Merit function 460. The availability of a well-definedreverse path can greatly increase the efficiency of the exploration andsearch.

FIG. 29A also illustrates an embodiment of the method 490(b), revertingto a preceding bifurcation in a search path in the Design Factor space.During the search, or exploration, at some points two choices may appearcomparably meritorious. In such instances, the patient, or theProgressive Lens Design Processor 320, needs to choose one of thepossibilities. FIG. 29A illustrates such a case, when the search pathencountered the locally optimal ridge splitting, or bifurcating, intotwo, comparable ridges. The search method 465, possibly with its localembodiment 475, chose the left branch and pursued the exploration for awhile. However, after monitoring the exploration on the left ridge for awhile, the SGE 330 decided that is was time to practice the method490(a) and reversed the search. Advantageously, the quantitative LensMerit function 460 provided clear guidance how to pursue the reversedpath: by retracing the ridge of the LM 460. Retracing the ridge leadsthe search back to the bifurcation, where previously the search selectedthe left ridge. At this junction, the Search Guidance Engine 330 now maychoose the right ridge instead and then re-engage the local modifyingmethod 475 to guide the search along the other ridge. Throughout thesesteps of the methods 475 and 490, the quantitative Lens Merit function460 provided clear guidance and greatly narrowed the searches to bepursued (from exploring surfaces to exploring ridges), thereby enhancingthe efficiency and speed of the overall exploration.

FIG. 29B illustrates that in some embodiments, the method 490 can beexpanded to include method 495 that includes performing the SearchManagement step with a Search Guidance Engine 330 interactively by

(a) proposing to an operator to select a Search Management step;

(b) receiving a selection of a Search Management step from the operator;and

(c) initiating the execution of the selected Search Management step.

As with the interactive method 455 earlier, the method 490 can also beimproved by performing it interactively. Instead of the Progressive LensDesign Processor 320 and the Search Guidance Engine 330 of the LDES 300making choices by some algorithm, the method 495 prompts the patient, oroperator, to provide Visual Feedback 420, command or control signal, andto select a Search Management step, thereby accelerating the lens designexploration via executing steps 495(a)-(b)-(c).

In some further embodiments, the modifying the progressive lens designcan include modifying the progressive lens design by the ProgressiveLens Design Processor 320 utilizing non-linear Visual Feedback-to-DesignFactor functional relations, or non-linear Visual Feedback+Merit-to-Design Factor functional relations. These embodiments can bealternatives and complementary of the Matrix methods 445 and 470, wherethese relations are dominantly linear.

Some embodiments of the PLS 100+ LDES 300 system and the method 400 ofits operation can include additional elements and steps that make itessentially certain that the overall method 400 will not deliverinferior results for the patient relative to traditional practices.These additional steps can include determining a second, “traditional”progressive lens design that is based on traditional measurements thatdo not involve simulating the second progressive lens design for thepatient and asking for his/her Visual Feedback. This can be followed bygenerating a Comprehensive PLS 30 with this second, traditionalprogressive lens design. In such embodiments, the patient gets toexperience the progressive lens design the traditional approachdelivers, as well as the lens design identified by method 400 with thehelp of PLS 100+ LDES 300. Generating the two differently designedprogressive lenses, one with simulation and feedback, the other withtraditional measurements, the patient can compare the Comprehensive PLSof these two designs, and select his/her preference. With this extensionof the method 400, the patient cannot end up with a lens worse than thetraditional progressive lens, as he/she can elect the traditionalprogressive lens anytime, even at the very end of the office visit, ifhe/she is unsatisfied with the lens design determined by the method 400.

In some cases, the generating 402 an image 21 by an Image Generator 121can be in response to an image selection. This additional step can againimprove patient satisfaction, as the patients can select images that arerelevant for their activities, instead of the well-known rows of letterswhich do not capture life-like applications. For example, a team sportsperson may need extra emphasis on peripheral vision, and thus can selectan image 21 that has notable features located peripherally. Along-distance truck driver may select moving images, as she/he mayprioritize optimizing her/his vision for moving images. A night guardmay select images with low light conditions, as she/he may need tooptimize his vision for low light circumstances. Offering to test apatient's vision on use-relevant images can further improve thecustomization and thereby the satisfactory outcome of the method 400.

7. Deep Learning Method for a Progressive Lens Simulator with anArtificial Intelligence Engine

Translating the Visual Feedbacks 430 into the Design Factors 420 mosteffectively is a substantial challenge. The various methods 400-495offer promising embodiments. To make these embodiments the mostefficient, their many parameters need to be optimized. These include theelements of the Visual Feedback-to-Design Factor matrix 326 and theelements of the Visual Feedback+ Lens Merit-to-Lens Design matrix 329.For other embodiments, the possibly non-linear connections between theVisual Feedbacks 430 and the Design Factors 420 need to be tabulated andparameterized. Also, the Search Guidance Engines 330 are best operatedon the basis of past experiences. The Search Management methods 450/455and 490/495 can also be improved by learning from the lessons of thepreceding explorations. Finally, performing the local modification steps475 and non-local modification steps 480 can be done most efficiently byutilizing the lessons of past searches.

Recently, entirely new ways of learning from past experiences have beenautomated in remarkably effective new ways. In various technicaltreatises, these ways are called Artificial Intelligence, Deep Learning,Machine Learning, or Neural Networks. Other names are also used tocapture roughly the same learning methods. The embodiments in thissection integrate Artificial Intelligence-based teaming methods andsystems into the GPS 10 so as to improve and optimize the manyparameters of the matrices 326 and 329, the operations of the SGE 330and the methods of 450/455 and 490/495, as well as the other areas wherelearning from past experiences is beneficial.

FIG. 30 illustrates such a Guided Lens Design Exploration System ofSimulated Progressive Lenses (GPS) 10. This GPS 10 can include anyembodiment of the Multistage PLS 100, Integrated PLS 200, orhead-mounted PLS 260. FIG. 30 illustrates these possibilities via themultistage PLS 100 embodiment as an example. The PLS 100 can be combinedwith any, previously described embodiment of the Lens Design ExplorationSystem for the PLS LDES 300. The combination of the PLS 100 and the LDES300 can be integrated with an Artificial Intelligence (AI) Engine forthe GPS (AI-GPS) 500. In this section, the GPS 10 will reference thecombination of any embodiment of the PLS 100 with the LDES 300 and theAI-GPS 500.

The AI-GPS 500 can include an AI Engine for the Progressive Lens DesignProcessor (AI-PLD) 510, integrated into, or at least coupled to theProgressive Lens Design Processor 320. The AI-PLD 510 can carry out mostof the functions of the Visual Feedback-to-Lens Design Transfer EngineFLE 325, and thus can be viewed as an embodiment of the FLE 325. Afunction of the AI-PLD 510 can be to perform any one of the many knownDeep Learning methods in order to make translating the Visual Feedbacks430 into modifications of the Design Factors 420 more efficient. Asindicated, these Deep Learning methods can repeatedly update and improvethe matrix elements of the Visual Feedback-to-Design Factor matrix 326,and the matrix elements of the Visual Feedback+ Lens Merit-to-LensDesign matrix 329.

In some embodiments, the GPS 10 can also include an AI Engine for theSearch Guidance Engine (AI-SGE) 530, integrated into, or at leastcoupled to the Search Guidance Engine SGE 330. A function of the AI-SGE530 can be to perform any one of the many known Deep Learning methodswith the SGE 330 in order to make it more efficient in guiding theexploration of the progressive lens designs 123 by any of the describedmethods. These methods include method step 450: performing a SearchManagement step with the SGE 330; method step 455: performing the methodstep 450 interactively with a patient; performing the method step 490:performing a Search Management step 330 with the SGE 330 that involves aLens Merit 460; and method step 495—performing the method step 490interactively with a patient, among others.

Finally, the AI-GPS 500 can also include an AI Engine for theProgressive Lens Simulator (AI-PLS) 550, integrated into, or at leastcoupled to PLS 100, in some case integrated into its OPS processor 122.Embodiments of the AI-PLS 550 can perform any of the known AI DeepLearning methods with the OPS Processor 122 to simulate the progressivelens designs 123 better, learning from the Visual Feedbacks 430 of thepatients.

FIGS. 31-33 illustrate an embodiment of the AI-PLD 510. The AI-SGE 530and the AI-PLS 550 can have closely analogous designs and will not beexpressly described to avoid triplication.

The AI-PLD 510 can include an input layer 515, configured to receive theVisual Feedbacks VF_(i)(n) as inputs. (In what follows, the label 430will not always be expressly shown for the Visual Feedbacks 430, and thelabel 420 for the Design Factors 420, in order to avoid clutter). Thepatient, the operator or an objective feedback generating system, suchas the Eye tracker 110, or any other eye imaging system can input theVF_(i) elements of the Visual Feedback vector VF(n). As before, “n”references the n^(th) iteration of the lens design exploration method400. A Visual Feedback-Design Factor VFDF Neural Network 520 can receivethe VF_(i)(n) Visual Feedbacks as inputs. This VFDF Neural Network 520can be configured to output a proposed update of the Design Factorvector ΔDF(n)=DF(n+1)−DF(n) at an output layer 525. In this sense, theVFDF Neural Network 520 plays an analogous function as the VisualFeedback-to-Lens Design Factor Engine FLE 325.

One of the differences is that the input VF vector 430 is not connectedto the output DF vector 420 by a linear VFDF matrix 326. Instead, it isgenerated by a series of transformations, performed by a series ofhidden layers 521, 522, 523, as shown. Each hidden layer can include aset of neurons. In the shown example, the first hidden layer 521includes neurons N₁₁, N₁₂, . . . N₁₆. In this description, the specificnumber of hidden layers, and the number of neurons in each layer is forillustrational purposes only. Other embodiments may contain any othernumber of hidden layers and neurons per layer.

The neurons N_(1i) in the first layer can be coupled to the neurons ofthe second layer N_(2j), with coupling strength C(1 i-2 j).

FIG. 32 illustrates the operation of the VFDF Neural Network 520. Theeffect of the Visual Feedback vector elements VF_(i) getting processedby the neurons of a hidden layer is to multiply the VF vector with aCoupling Matrix CM. Here the notation is used that the (ij) element ofthe CM(m) matrix is the coupling constant between the neuron N_(mi) andN_((m+1)j). With this notation, the above referenced coupling constantC(1 i-2 j) is CM(1)_(ij), the (ij) element of the CM(1) matrix. Thus,the output of a VFDF Neural Network 520 with K hidden layers can bewritten as:VF(n)^(T)*CM(1)*CM(2)* . . . *CM(K)=ΔDF(n)^(T),  (2)

where the superscripts T in VF(n)^(T) and ΔDF(n)^(T) indicate vectortransposition. This is needed, as in FIG. 22A the vectors weremultiplied with the matrices from the left, while in FIG. 32 from theright, to represent the left-to-right layer-to-layer processing of theVisual Factor vector VF 430 in FIG. 31. Obviously, these twodescriptions are equivalent.

In words, the output vector ΔDF 420 is related to the input vector VF430 not by a single linear relation, as in the case of the VFDF matrix326, but by a product of several bilinear relations. It is noted thatthe dimensions of the individual coupling matrices can vary in thisproduct, or chain, of coupling matrices. Selecting these dimensionswisely can enhance the efficiency of the lens design exploration. Forexample, there are AI methods that advocate initially shrinking thedimensions of the CM(i) matrices, then increasing them back up, forminga sort of tunnel, or spool.

The neurons in each layer can be set up to react to their input in avariety of ways. A simple, widely used algorithm is a sharp thresholdinput-output relation output=ƒ(input). The inputs from the precedinglayer i to a particular neuron N_((i+1)l) in the (i+1)^(th) layer can beadded up, and if this sum exceeds a threshold T, then the neuronN_((i+1)l) outputs a value, whereas if the sum does not exceed thethreshold T, then the output remains a different, lower number,typically zero. In formula,N _((i+1)l)=ƒ(Σ_(j) N _(ij)CM(i)_(jl) −T),  (3)

where ƒ(x) is a sharp switching function of its argument x, typically astep function centered at 0, or a somewhat smoothed step function such atanh(x). A motivation for this switching function ƒ(x) emerged from theoperations of neurons: the inputs from several dendrites that bring inthe inputs in the form of stimuli are often added up by the electriccharging processes of the neuron. As carried out by the ion pumps acrossthe cell membrane. If the sum of these stimuli, often in the form of atotal charge, exceeds a threshold, then the output axon of the neuronfires. If the charging does not reach the threshold, the neuron does notproduce an output.

One way to set up such a VFDF Neural Network 520 is Google's TensorFlowmachine learning framework. This is a convenient sophisticated frameworkthat can be set up with limited preparation. A designer of the VFDFNeural Network 520 can simply enter the number of hidden layers, thenumber of neurons in each layer, and the switching functions ƒ(x). Then,the initial coupling constants of the VFDF Neural Network 520 can beentered or defined.

After this initialization, the VFDF Neural Network 520 is driven throughmany teaching, or learning cycles so that the VFDF Neural Network 520learns from incorrect, or low fidelity results, and the initial couplingconstants “learn” and evolve to reduce the incorrect outputs and producethe correct output in a high percentage of the cases.

FIG. 33 illustrates one such teaching, or learning approach, calledBack-propagation with Gradient Descent (BGD) 510T. In such a supervisedlearning method, a Visual Feedback vector VF 430 is inputted where thesupervisor knows what output is expected. As an example, if the VisualFeedback 430 is that the image is blurry in the lower nasal quadrantafter the refractive power has been increased in this quadrant in thefew preceding cycles, then the expected output ΔDF is to keep increasingthe refractive power in this quadrant. With this known expectation, thisVF is inputted into the input layer 515 and then processed with the VFDFNeural Network 520, resulting in an output at the output layer 525. Anoutput evaluator 526 then can compare the output with the targetedoutput and send the difference signals Δ_(i)(n+1) back through the sameVFDF Neural Network 520 as an error-back-feed 527. In the given example,whether the VFDF Neural Network 520 outputted a “Increase the refractivepower further in the lower nasal quadrant” ΔDF(n) modification of theDesign Factor vector DF 420, and what power increase was outputtedspecifically. As the difference signal is propagating backwards, acoupling trainer 528 can modify the individual couplings CM(i)_(k1) toreduce the difference. Performing these training cycles a large numberof times with a large number of inputted visual feedback vectors VF 430trains the elements of the Coupling Matrices CM(1), CM(2), . . . CM(K)so that after the training, when new Visual Feedbacks VFs are inputtedfor which the outputs are not known, the VFDF Neural Network 520 willstill output the most appropriate ΔDF with high reliability.

Training the AI Engine AI-GPS 500 with this Back-propagation withGradient Descent (BGD) 510T is just one of the many possible methods totrain this VFDF Neural Network 520. Also, using this particular NeuralNetwork implementation of the AI-GPS 500 is just one of the manyembodiments of the basic idea to utilize Artificial Intelligence systemsand methods to improve the GPS 10. All these combinations andalternatives share the following two basic design principles, and can beviewed as embodiments of the GPS 10 system, as long as they do so. Thesedesign principles include the followings.

(1) To fine tune the large number of parameters needed to operate theGPS 10 system, such as the elements of the VFDF matrix 326 or the VisualFeedback+ Lens Merit-to-Design Factor matrix 329, utilizing theremarkable power of Artificial Intelligence in any suitable AI-GPSengine 500.

(2) To continue developing and improving these GPS 10 systems as arapidly increasing number of GPS 10 s are installed worldwide. It isenvisioned that a centralized system may collect the exponentiallyincreasing amount of data on the many-many lens design explorationsperformed worldwide daily by a rapidly increasing number of patients.This exponentially increasing “big data” can be analyzed by centralAI-GPS engines 500. The results of this analysis can be pushed out fromthe central system to the individual GPS 10 systems in the form ofupdates for the on-board search software, including the training of theelements of the matrices 326 and 329, the training of the various stepsthat can be involved in the Search Management 450/455 and 490/495, andthe training of the simulation of the progressive lens designs 123 bythe PLS 100 s. Any embodiment that shares these two basic drivers is anembodiment of the GPS 10.

FIGS. 34-35 illustrate methods of operation of the AI-GPS engines 510and 530. FIG. 34 illustrates a Method 610 of operating a ProgressiveLens Simulator 100 with an Artificial Intelligence Engine 500,comprising:

(a) generating 611 a Comprehensive Progressive Lens Simulation(Comprehensive PLS) 30 for a patient with a Progressive Lens Simulator100, based on a progressive lens design 123 with a Design Factor vectorDF 420, generated by a Progressive Lens Design Processor 320;

(b) receiving 612 a Visual Feedback vector VF 430 into a VisualFeedback-Design Factor Neural Network 520 of an Artificial IntelligenceEngine for the Progressive Lens Design Processor AI-PLD 510, in responseto the Comprehensive PLS 30; and

(c) outputting 613 a modification ΔDF of the Design Factor vector DF 420with the Visual Feedback-Design Factor Neural Network 520, in responseto the receiving 612; wherein

(d) coupling matrices CM(i) of the Visual Feedback-Design Factor NeuralNetwork 520 were trained by performing 614 a deep learning cycle.

The method 610, further comprising:

(e) modifying the progressive lens design 123 by the Progressive LensDesign Processor 320 using the modified Design Factor vector DF 420; and

(f) generating a modified Comprehensive PLS 30 by the Progressive LensSimulator 100, using the modified progressive lens design 123.

Repeating steps (b)-(f) iteratively can serve as the backbone of theexploration of the progressive lens designs, as described in relation tomethod 400.

The Visual Feedback-Design Factor Neural Network 520 can include layersof neurons N_(ij), including an input layer 515, one or more hiddenlayers 521/522/523, and an output layer 525. The neurons N_(ij) canhaving switching functions f(x), and the neurons N_(ij) can be coupledby the coupling matrices CM(i).

The performing a learning cycle step 614 may include performing the deeplearning cycle by Backpropagation with Gradient Descent (BGD) 510T.Performing the deep learning cycle step with BGD 510T can fine tune theelements of the Coupling matrices CM(i)_(k1) to provide the mostreliable translation of the Visual Feedbacks 430 into changes of theDesign Factor vector 420.

The performing a learning cycle step 614 may also include using 615 aLens Merit function LM 460. Using a Lens Merit function LM 460 canenable the AI-GPS engine 500 and specifically the AI-PLD 510 to trainand improve the Lens Merit based methods 465-495.

The performing a deep learning cycle 614 can also include evaluating 616the outputted Design Factor vector DF 420 with an output evaluator 526in relation to a target Design Factor vector DF 420 corresponding to theinputted Visual Feedback vector VF 430; and training the couplingmatrices CM(i) according to the evaluating with a coupling trainer 528.

In some embodiments, the performing a deep learning cycle step 614 caninclude modifying 617 a software of the Progressive Lens DesignProcessor 320.

FIG. 35 illustrates a Method 620 of operating a Progressive LensSimulator 100 with an Artificial Intelligence Engine 500, comprising:

(a) generating 621 a Comprehensive Progressive Lens Simulation(Comprehensive PLS) 30 for a patient with a Progressive Lens Simulator100, based on a progressive lens design 123 generated by a ProgressiveLens Design Processor 320;

(b) receiving 622 a Visual Feedback vector VF 430 into a VisualFeedback-Search Management Neural Network 520-SGE of an ArtificialIntelligence Engine for a Search Guidance Engine AI-SGE 530, in responseto the Comprehensive PLS 30; and

(c) outputting 623 a Search Management step 450/455, or 490/495, withthe Visual Feedback-Search Management Neural Network 520-SGE to theProgressive Lens Design Processor 320, in response to the receiving 622;wherein

(d) coupling matrices CM(i) of the Visual Feedback-Search ManagementNeural Network 520-SGE were trained by performing a deep learning cycle624.

Here, and in what follows, the Visual Feedback-Search Management NeuralNetwork 520-SGE will be referenced with the same numerical labels as theVisual Feedback-Design Factor Neural Network 520, only with thesub-label “SGE” appended to it. This is to avoid needless repetition, asthe embodiments are largely analogous.

In some embodiments, the method 620 can also include the followings.

(e) modifying the progressive lens design 123 by the Progressive LensDesign Processor 320 prompted by the Search Management step 450/490; and

(f) generating a modified Comprehensive PLS 30 by the Progressive LensSimulator 100, using the modified progressive lens design 123.

Repeating steps (b)-(f) iteratively can serve as the backbone of theexploration of the progressive lens designs, as described in relation tomethod 400.

The Visual Feedback-Search Management Neural Network 520-SGE of theAI-SGE 530 can include layers of neurons N_(ij), including an inputlayer 515-SGE, one or more hidden layers 521/522/523-SGE, and an outputlayer 525-SGE; the neurons N_(ij), having switching functions ƒ(x), andthe neurons N_(ij) being coupled by the coupling matrices CM(i).

In some embodiments, the performing the deep learning cycle 624 caninclude performing the deep learning cycle by Backpropagation withGradient Descent BGD 510T-SGE.

In some embodiments, the performing a deep learning cycle 624 caninclude using 625 a Lens Merit function LM 460.

In some embodiments, the performing a deep learning cycle 624 caninclude evaluating 626 the outputted Search Management step 450/455 or490/495 with an output evaluator 526 in relation to a target SearchManagement step corresponding to the inputted Visual Feedback vector VF430; and training the coupling matrices CM(i) according to theevaluating with a coupling trainer 528.

By way of an example, the Search Management step can be 490(a), thereversing the search path. The decision that at which Visual Feedback VF430 to reverse the path can be very much a complex decision. Forexample, referring back to FIG. 29A, it needs to be determined how muchdoes the height of the ridge of the Lens Merit function LM 460 has todrop from its previous maximum for the AI-SGE 530 to activate the SearchManagement step 490(a). The AI-SGE 530 can be driven through many deeplearning cycles to find and learn the optimal value to reverse thesearch path.

Referring to FIGS. 28A-B is another example; when a search starts toshow less and less progress, the AI-SGE 530 needs to decide when toexecute a non-local jump, and whether the jump should be entirely randomor be driven by some consideration, e.g., the stored memory of anearlier portion of the search remembering the value of the Lens Meritfunction LM 460. As before, the AI-SGE 530 can be driven through manydeep learning cycles to find and learn when to initiate a jump, how biga jump to initiate, and how to relate the jump of data stored from theearlier search path.

In some embodiments, the performing a deep learning cycle 624 caninclude modifying 627 a software of the Search Guidance Engine 330.

In some embodiments, the AI-GPS 500 system may include a deep learningunit that uses an eye-model, such as the Holladay or Hofer eye model,wherein the AI-GPS 500 can train the parameters of the GPS 10 based ofthe eye model.

Finally, very analogous methods can be practiced to train and thenoperate the AI Engine for the Progressive Lens Simulator (AI-PLS) 550.This AI-PLS 550 can include a Visual Feedback-Lens Simulation NeuralNetwork 520-PLS, with components and elements very analogous shown inFIGS. 31-33, and operated analogously to FIGS. 34-35.

As mentioned earlier, the Artificial Intelligence Engine 500 can use anyof the known AI methods, and is not narrowed down to neural networksalone. Other AI methods include Supervised learning methods,Non-supervised learning, Regression analysis-based methods, Clustering,Dimensionality reduction, Structured predictions, Anomaly detection andReinforcement training. Any one of these AI methods can be implementedin the AI-GPS 500.

8. Central Supervision Station System for Progressive Lens Simulators

One of the substantial benefits of the various embodiments of the GPSsystem 10, for example, the PLS 100, or the PLS 100 combined with theLDES 300, or the PLS 100 and the LDES 300 combined with the ArtificialIntelligence Engine for GPS, AI-GPS 500, together referenced as the100/300/500 embodiments of GPS 10, is that many of the functionspreviously executed by an optometrist are now carried out by theautomated systems of the GPS 10. Therefore, an optometrist does not haveto be continuously involved in the exploration of progressive lensdesigns 123. The patient himself/herself can engage with the 100/300/500embodiments of the GPS 10 to execute any version of the explorationmethod 400, and take all the time he/she needs. The patient can go backto previously identified lens designs 123 and compare them with newones: can chose different images 21 with the Image generator 121; keepperforming Search Management steps 450/455 or 490/495 to retrace searchpaths, explore other choices, fix a Design Factor 420 to narrow thesearch, slow down the search in some region of the Design Factor space,and so on. All of these can be performed without active involvement byan optometrist.

From a time-management point of view, this aspect of the GPS 10 systemsfrees up an optometrist to such a degree that she/he can supervise morethan one individual GPS systems 10 simultaneously, thereby eliminatingthe need of manning each optometry station individually with anoptometrist. This aspect can substantially reduce the number ofpersonnel required to service a given number of patients, and thus canbe greatly helpful for the business model of the overall optometristoffice.

FIG. 36 illustrates this concept in some detail. FIG. 36 illustrates aSupervised Multi-station system 700 of Progressive Lens Simulators 100that comprises a Central Supervision Station 710; coupled to a set ofProgressive Lens Simulators 720-1, 720-2, . . . 720-n, (togetherreferenced as 720-i) by two-way communication-supervision channels730-1, 730-2, . . . 730-n, together referenced as 730-i. FIG. 36illustrates a three station (n=3) embodiment.

The individual stations can include the Progressive Lens Simulators720-i that can be any embodiment described earlier in this application,including the multistage PLS 100, the integrated IPLS 200, a table-topembodiment of a PLS 100, and the head-mounted PLS 260, this latterembodiment being shown in FIG. 36. In the shown embodiment, the PLS720-i can individually include an Eye Tracker 110, for tracking an eyeaxis direction to determine a gaze distance; an Off-Axis ProgressiveLens Simulator 120, for generating an Off-Axis progressive lenssimulation (Off-Axis PLS 20) of a progressive lens design 123; and anAxial Power-Distance Simulator ADS 130, for simulating a progressivelens power in the eye axis direction, thereby creating a ComprehensiveProgressive Lens Simulation (PLS) 30 of a progressive lens design 123from the Off-Axis PLS 20.

Some elements of the PLS 720-i can be implemented in the CentralSupervision station 710. The Central Supervision Station 710 can be incommunication with the Progressive Lens Simulators 720-i, for providingsupervision for an operation of the individual Progressive LensSimulators 720-i.

This communication can take place via the two-way communication channels730-i between the individual Progressive Lens Simulators 720-i and theCentral Supervision Station 710: the Progressive Lens Simulators 720-ican inform the Central Supervision Station 710 about simulations ofprogressive lens designs 123, and the Central Supervision Station 710supervising the simulation by the Progressive Lens Simulators 720-i. Thecommunication channels 730-i can be wired communication channels orwireless communication channels.

In some embodiments, the Progressive Lens Simulators 720-i canindividually comprise Lens Design Exploration Systems LDES 300, forguiding an exploration of progressive lens designs 123.

In other embodiments, the Central Supervision Station 710 can comprise acentralized Lens Design Exploration System LDES 300, for guiding anexploration of progressive lens designs, and communicating correspondingguidance signals to the individual Progressive Lens Simulators 720-i.

In some embodiments, the individual Progressive Lens Simulators 720-ican include dedicated individual Artificial Intelligence (AI) Enginesfor executing a deep learning method for Progressive Lens DesignProcessors 320-i of the Progressive Lens Simulators 720-i. These AIEngines can be embodiments of the AI Engines for the Progressive LensDesign Processors (A-PLD) 510.

In other embodiments, the Central Supervision Station 710 can comprise acentralized Artificial Intelligence Engine 500, for executing a deeplearning method for the Progressive Lens Design Processors 320 of theProgressive Lens Simulators 720-i, and communicating correspondingtraining signals to the individual Progressive Lens Simulators 720-i.

In some embodiments, the Progressive Lens Simulators 720-i canindividually comprise Artificial Intelligence Engines, for executing adeep learning method for Search Guidance Engines 330 of the ProgressiveLens Simulators 720-i. These AI Engines can be embodiments of the AIEngines for the Search Guidance Engines (AI-SGE) 520.

In other embodiments, the Central Supervision Station 720-i can comprisea centralized Artificial Intelligence Engine 500, for executing a deeplearning method for a centralized Search Guidance Engine, andcommunicating corresponding guiding signals to the individualProgressive Lens Simulators 720-i. The Search Guidance Engine can bealso centralized, or can reside in the individual PLS 720-i asindividual SGEs 330-i.

Similarly, an AI Engine can be included for executing a deep learningmethod for the Off-Axis PLS 120. As before, this AI Engine can becentralized, or can reside with the individual PLS 720-i.

While this document contains many specifics, these should not beconstrued as limitations on the scope of an invention or of what may beclaimed, but rather as descriptions of features specific to particularembodiments of the invention. Certain features that are described inthis document in the context of separate embodiments can also beimplemented in combination in a single embodiment. For example, tostructure the presentation more clearly, the description of theembodiments was organized into eight sections. However, the features ofthe embodiments in any one of these sections can be combined withfeatures and limitations of any embodiments from the other sevensections. Conversely, various features that are described in the contextof a single embodiment can also be implemented in multiple embodimentsseparately or in any suitable subcombination. Moreover, althoughfeatures may be described above as acting in certain combinations andeven initially claimed as such, one or more features from a claimedcombination can in some cases be excised from the combination, and theclaimed combination may be directed to a subcombination or a variationof a subcombination.

The invention claimed is:
 1. A Method of operating a Progressive LensSimulator with an Artificial Intelligence Engine, comprising: generatinga Comprehensive Progressive Lens Simulation (Comprehensive PLS) for apatient with a Progressive Lens Simulator, based on a progressive lensdesign with a Design Factor vector, generated by a Progressive LensDesign Processor; receiving a Visual Feedback vector into a VisualFeedback-Design Factor Neural Network of an Artificial IntelligenceEngine for the Progressive Lens Design Processor (AI-PLD), in responseto the Comprehensive PLS; and outputting a modification of the DesignFactor vector with the Visual Feedback-Design Factor Neural Network tothe Progressive Lens Design Processor to manage a progressive lensdesign exploration, in response to the receiving; wherein couplingmatrices of the Visual Feedback-Design Factor Neural Network weretrained by performing a deep learning cycle.
 2. The method of claim 1,comprising: modifying the progressive lens design by the ProgressiveLens Design Processor using the modified Design Factor vector; andgenerating a modified Comprehensive PLS by the Progressive LensSimulator, using the modified progressive lens design.
 3. The method ofclaim 1, wherein the Visual Feedback-Design Factor Neural Networkcomprises: layers of neurons, including an input layer, one or morehidden layers, and an output layer; the neurons having switchingfunctions, and the neurons being coupled by the coupling matrices. 4.The method of claim 1, the performing the deep learning cyclecomprising: performing the deep learning cycle by Backpropagation withGradient Descent.
 5. The method of claim 4, the performing a deeplearning cycle comprising: using a Lens Merit function.
 6. The method ofclaim 1, the performing a deep learning cycle comprising: evaluating theoutputted modification of the Design Factor vector with an outputevaluator in relation to a target Design Factor vector corresponding toan inputted Visual Feedback vector; and training the coupling matricesaccording to the evaluating with a coupling trainer.
 7. The method ofclaim 1, the performing a deep learning cycle comprising: modifying asoftware of the Progressive Lens Design Processor.
 8. A Method ofoperating a Progressive Lens Simulator with an Artificial IntelligenceEngine, comprising: generating a Comprehensive Progressive LensSimulation (Comprehensive PLS) for a patient with a Progressive LensSimulator, based on a progressive lens design generated by a ProgressiveLens Design Processor; receiving a Visual Feedback vector into a VisualFeedback-Search Management Neural Network of an Artificial IntelligenceEngine for a Search Guidance Engine (AI-SGE), in response to thecomprehensive PLS; and outputting a Search Management step with theVisual Feedback-Search Management Neural Network to the Progressive LensDesign Processor to provide guidance in an exploration of progressivelens design, in response to the receiving; wherein coupling matrices ofthe Visual Feedback-Search Management Neural Network were trained byperforming a deep learning cycle.
 9. The method of claim 8, comprising:modifying the progressive lens design by the Progressive Lens DesignProcessor prompted by the Search Management step; and generating amodified Comprehensive PLS by the Progressive Lens Simulator, using themodified progressive lens design.
 10. The method of claim 8, wherein theVisual Feedback-Search Management Neural Network comprises: layers ofneurons, including an input layer, one or more hidden layers, and anoutput layer, the neurons having switching functions, and the neuronsbeing coupled by the coupling matrices.
 11. The method of claim 8, theperforming the deep learning cycle comprising: performing the deeplearning cycle by Backpropagation with Gradient Descent.
 12. The methodof claim 11, the performing a deep learning cycle comprising: using aLens Merit function.
 13. The method of claim 8, the performing a deeplearning cycle comprising: evaluating the outputted Search Managementstep with an output evaluator in relation to a target Search Managementstep corresponding to the inputted Visual Feedback vector; and trainingthe coupling matrices according to the evaluating with a couplingtrainer.
 14. The method of claim 8, the performing a deep learning cyclecomprising: modifying a software of the Search Guidance Engine.
 15. AGuided Lens Design Exploration System of Simulated Progressive Lenses(GPS), comprising: a Progressive Lens Simulator, including an EyeTracker, for tracking an eye axis direction to determine a gazedistance; an Off-Axis Progressive Lens Simulator, for generating anOff-Axis progressive lens simulation (Off-Axis PLS) of a progressivelens design; and an Axial Power-Distance Simulator, for simulating aprogressive lens power in the eye axis direction, thereby creating aComprehensive Progressive Lens Simulation of the progressive lens designfrom the Off-Axis PLS; a Lens Design Exploration System for theProgressive Lens Simulator; and an Artificial Intelligence (AT) Engine,integrated into the GPS (AI-GPS), trained by performing deep learningcycles to provide guidance in an exploration of progressive lensdesigns; wherein an operation of the GPS system comprises: generatingthe Comprehensive e Progressive Lens Simulation (Comprehensive PLS) fora patient with the Progressive Lens Simulator, based on the progressivelens design generated by a Progressive Lens Design Processor; receivinga Visual Feedback vector into a Visual Feedback-Search Management NeuralNetwork of an Artificial intelligence Engine for a Search GuidanceEngine (AT-SGE), in response to the Comprehensive PLS; and outputting aSearch Management step with the Visual Feedback-Search Management NeuralNetwork to the Progressive Lens Design Processor, in response to thereceiving, wherein the coupling matrices of the Visual Feedback-SearchManagement Neural Network were trained by performing the deep learningcycle.
 16. The Guided Lens Design Exploration System of SimulatedProgressive Lenses (GPS) of claim 15, wherein the operation of the GPSsystem further comprises: generating the comprehensive Progressive LensSimulation (Comprehensive PLS) for a patient with the Progressive LensSimulator, based on the progressive lens design with a Design Factorvector, generated by a Progressive Lens Design Processor; receiving aVisual Feedback vector into a Visual Feedback-Design Factor NeuralNetwork of an Artificial Intelligence Engine for the Progressive LensDesign Processor (AI-PLD), in response to the Comprehensive PLS; andoutputting a modification of the Design Factor vector with the VisualFeedback-Design Factor Neural Network, in response to the receivingwherein the coupling matrices of the Visual Feedback-Design FactorNeural Network were trained by performing the deep learning cycle.